maintenance scheduling and vehicle routing optimisation
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Maintenance Scheduling and Vehicle RoutingOptimisation with Stochastic Components
Andres Felipe Gutierrez Bonilla
To cite this version:Andres Felipe Gutierrez Bonilla. Maintenance Scheduling and Vehicle Routing Optimisation withStochastic Components. Operations Research [cs.RO]. Université de Technologie de Troyes; Universi-dad de los Andes (Bogotá), 2018. English. NNT : 2018TROY0023. tel-03212070
THESE
pour l’obtention du grade de
DOCTEUR de l’UNIVERSITE
DE TECHNOLOGIE DE TROYES
Spécialité : OPTIMISATION ET SURETE DES SYSTEMES
présentée et soutenue par
Andres Felipe GUTIERREZ BONILLA
le 10 juillet 2018
Maintenance Scheduling and Vehicle Routing Optimisation
with Stochastic Components
JURY
M. C. BRIAND PROFESSEUR DES UNIVERSITES Président
M. C. A. AMAYA PROFESOR ASOCIADO Rapporteur
Mme L. DIEULLE MAITRE DE CONFERENCES Examinateur
M. D. FEILLET PROFESSEUR MINES SAINT-ETIENNE Rapporteur
Mme N. LABADIE MAITRE DE CONFERENCES - HDR Directrice de thèse
M. C. PRINS PROFESSEUR DES UNIVERSITES Examinateur
Mme N. VELASCO PROFESORA ASOCIADA Directrice de thèse
Acknowledgments
First of all I will like to thank You. After all you are the purpose and reason of my life. Then, I
would like to thank my advisers Nacima Labadie and Nubia Velasco. Although there are no words to
describe how grateful I am, I will give it a try. I have learned with you a lot about my thesis, solution
methods, problem modeling, and generally speaking about the scientic community. But also, I have
learned much more about life. Your support through all this years was essential for me. Even with
all my mistakes (and missed deadlines) you always had the words to motivate me. You were the best
advisers I could dream for, and my gratitude and friendship is yours forever. Also I would like to thank
Laurence Dieulle, my own third adviser. Your support and advice during the thesis was incredible.
Our discussions were always helpful for me to develop new ideas, even if sometimes I found it hard to
express the details of the methods I was thinking about. I have learned so many things with you and
I expect that you have learned at least a little bit with me.
An special word is given to thank Dominique Feillet, Ciro Amaya, Cyril Briand, and Christian
Prins for being part of the jury. It is both an honor and a motivation to present to you the results of
these years of my life. I really appreciate all the comments and discussions that we have during the
defense. They gave me lights to open my research. Besides, I owe my gratitude to the professors and
the administrative body of both LOSI laboratory at the University of Technology of Troyes, and from
Management School at Los Andes University. Special thanks to professor Carlos Davila who taught
me so much about history and critical thinking. Likewise I thank Véronique, Bernadette, Pascale,S
Isabelle, Ingrid, Juan Carlos, Fredy, and Adriana, for all the help through all these years.
Also I would love to thank my family. Dad and Mom, it was due to you that I could make it up
to here. There is still a lot more to conquer but every success in my life is to honor you. You are the
kindest people that I have ever met and I only hope to follow your steps. To my brothers, Reuben,
Juan Carlos and Miguel, their wives Sonia and Sabina, and my beautiful nephews Sebastian, Martin,
Emilio, Arian, and the coming one. You are the engine of my dreams. Camila, from all the people,
you might be the one who suered the most my frustrations, sadness and worries. You, always had
the right words to comfort me, your voice always has the power to cheer me up, and forever I will love
you. Your family will be always mine too so Ruth, Juan Jose, Majo, Camilo, Sebas, Juan Pablo, thank
you very much. I can not forget Santi and Irene, my uncle Oscar and my ants Heidy and Rosario,
even at the distance you were always at my side.
In addition, I want to acknowledge my friends that make the life at Troyes and at Bogota much
more fun. Colombia team number one, Jorge, Caro, Karen, Guille, Adri, Elyn, Syrine, Marie, and
Colombian team number two, Andrea, Felipe, David, Aleja, Samir, So, Nacef, and Mourad. It was
always great to go out with you. Back to Colombia my special gratitude to Fabian (Crazy) and
Enrique. Your advice and the challenging and stimulating conversations always keep pushing the
boundaries of my knowledge. There are a lot more people that were fundamental in this process. If
I forget somebody it was not on purpose and I hope life will let me made up to you. Additionally,
I thank the anonymous referees that reviewed my work through these years, your comments always
helped to improve our work, and though your names are nor revealed, you will never be forgotten.
Finally, this thesis was possible thanks to the nancial support of the Champagne-Ardenne Region in
France, and COLCIENCIAS in Colombia, I praise your aid to science development.
i
ii
Contents
1 Introduction 1
2 Literature Review - Vehicle Routing Problems 7
2.1 Deterministic VRP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.1 Capacitated Vehicle Routing Problem . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.2 VRP with Time Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.3 Solution Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 VRP under uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.1 Stochastic VRP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.2 Robust Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.2.3 Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3 Hybrid metaheuristic for the VRPSD 41
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3 Solution approach: hybrid metaheuristic . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.1 Chromosomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.2 Initial population and Restart . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3.3 Crossover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.3.4 Mutation and Local Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.4 Numerical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.4.1 Classical testbed from Christiansen and Lysgaard . . . . . . . . . . . . . . . . . 49
3.4.2 New proposed testbed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4 Vehicle Routing Problem with stochastic travel and service times 67
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.2 Problem Denition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.3 Estimation of arrival times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.3.1 Arrival and starting service times denition . . . . . . . . . . . . . . . . . . . . 71
4.3.2 Mean and Variance estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.3.3 Validating the log-normality approximation . . . . . . . . . . . . . . . . . . . . 73
4.4 Multi-population Memetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.4.1 MA general structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.4.2 Chromosomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.4.3 Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.4.4 Crossover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.4.5 Local search and mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.4.6 MPMA framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
iii
4.4.7 MA dierences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.5 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.5.1 Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.5.2 General discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.5.3 The eects of considering multiple populations . . . . . . . . . . . . . . . . . . 83
4.5.4 MPMA + Log-normal approximation comparisons . . . . . . . . . . . . . . . . 83
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5 Wind farms maintenance 95
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.2 Review on operational maintenance activities . . . . . . . . . . . . . . . . . . . . . . . 96
5.3 Operational maintenance level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.4 A multi-objective approach to the maintenance scheduling problem . . . . . . . . . . . 100
5.4.1 Problem Denition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.4.2 Mathematical model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.4.3 The Epsilon constraint approach . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.5 Main contributions on strategic decision level . . . . . . . . . . . . . . . . . . . . . . . 106
5.5.1 Maintenance strategy selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
5.5.2 Complex models for maintenance strategy selection . . . . . . . . . . . . . . . . 109
5.6 Maintenance strategies: relation with operational planning . . . . . . . . . . . . . . . . 112
5.6.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.6.2 Simulation model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.6.3 Weather model - Wind speeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.6.4 Schedule Modeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.6.5 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.6.6 Model implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.6.7 Simulation model results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6 Conclusions 129
Appendices 133
A Résumé en français 135
A.1 Introduction aux VRP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
A.2 Une méthode hybride pour les VRP avec demandes stochastiques . . . . . . . . . . . . 140
A.3 Un algorithme parallèle pour les VRP avec temps de trajet et temps de service stochas-
tiques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
A.4 Gestion des ressources pour la maintenance d'un parc d'éoliennes . . . . . . . . . . . . 149
A.4.1 Ordonnancement multi objective pour le problème de maintenance des éoliennes 149
A.5 Sélection des stratégies de maintenance et relation avec l'ordonnancement . . . . . . . 151
A.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
B List of contributions 159
iv
List of Figures
2.1 Solution approaches scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1 Split example for the VRPSD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2 Broken pairs distance example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3 Avg. gap of MA+GRASP against the lling coecient . . . . . . . . . . . . . . . . . . 59
3.4 Avg. gap of MA+GRASP against the number of vehicles in the BKS . . . . . . . . . . 60
3.5 Avg. gap of MA+GRASP and number of vehicles against the number nodes per instance 60
3.6 MTTT Plots for the dierent MA methods - 5% and 1% . . . . . . . . . . . . . . . . . 62
3.7 MTTT Plots for the dierent MA methods - 0.5% and 0% . . . . . . . . . . . . . . . . 63
4.1 Arrival and starting service times example. . . . . . . . . . . . . . . . . . . . . . . . . 72
4.2 Average absolute error of 95% percentile with service and travel time noises. . . . . . . 75
4.3 Split example for the VRP with stochastic travel and service times . . . . . . . . . . . 77
4.4 Example of OX crossover. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5.1 Approximate Pareto Front for Froger et al. [31] instance 10_2_1_20_B_5 . . . . . . 105
5.2 Scheme of the simulation modules. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.3 An example of a turbine modeled a multi-component system. . . . . . . . . . . . . . . 115
5.4 Total maintenance costs by strategy/rule for the wind farm simulation. . . . . . . . . . 118
5.5 Total number of failures by strategy/rule for the wind farm simulation. . . . . . . . . . 119
5.6 Total produced energy under dierent strategy/rules for the wind farm simulation. . . 119
5.7 Mean turbine availability for dierent strategy/rules for the wind farm simulation. . . 120
5.8 Temporal analysis for wind farm metrics under dierent strategies. . . . . . . . . . . . 120
A.1 Classication des méthodes de résolution pour les VRP. . . . . . . . . . . . . . . . . . 137
A.2 grapheique MTT pour les versions de MA - 5% and 1% . . . . . . . . . . . . . . . . . 143
A.3 grapheique MTT pour les versions de MA - 0.5% and 0% . . . . . . . . . . . . . . . . 143
A.4 Temps d'arrivée et de début des services chez un client. . . . . . . . . . . . . . . . . . 145
A.5 Solutions Pareto optimales pour l'instance 10_2_1_20_B_5 de Froger et al. [11] . . 151
A.6 Coûts par stratégie et par règle d'assignation. . . . . . . . . . . . . . . . . . . . . . . . 153
A.7 Énergie produite par stratégie et par règle d'assignation. . . . . . . . . . . . . . . . . . 153
v
vi
List of Tables
2.1 VRPs taxonomy based on Pillac et al. [139] - Information evolution and quality . . . . 14
2.2 Summary of VRPSD literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Summary of VRPST literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.1 VRPSD Christiansen and Lysgaard [7] Testbed comparison . . . . . . . . . . . . . . . 49
3.2 Christiansen and Lysgaard [7] testbed results . . . . . . . . . . . . . . . . . . . . . . . 51
3.2 Christiansen and Lysgaard [7] testbed results: Continued . . . . . . . . . . . . . . . . 52
3.3 Summary of new testbed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.4 VRPSD Proposed testbed results - Costs . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.5 VRPSD Proposed testbed results - Time, Gaps . . . . . . . . . . . . . . . . . . . . . . 57
3.6 Two-way test values for Friedman method . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.7 Friedman test ranks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.1 Basic experiment information per family of instances . . . . . . . . . . . . . . . . . . . 74
4.2 Arrival times average absolute gaps between simulated values and log-normal approx-
imation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.3 Arrival times mean and standard deviation absolute gaps between simulated values and
three approximations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.4 Best solutions found by MPMA for C type instances . . . . . . . . . . . . . . . . . . . 82
4.5 Best solutions found by MPMA for R type instances . . . . . . . . . . . . . . . . . . . 82
4.6 Best solutions found by MPMA for RC type instances . . . . . . . . . . . . . . . . . . 82
4.7 Average performance of MPMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.8 Comparison of single MA1 to MPMA - 100 customer instances . . . . . . . . . . . . . 84
4.9 MPMA comparison to Miranda and Conceição [34] ILS . . . . . . . . . . . . . . . . . 84
4.10 MPMA comparison to Nguyen et al. [39] TS . . . . . . . . . . . . . . . . . . . . . . . 85
4.11 50 Customer instances best and average solutions found by MPMA per instance . . . 87
4.11 50 Customer instances best solutions and average found by MPMA per instance: Con-
tinued . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.12 100 Customer instances best solutions and average found by MPMA per instance . . . 89
4.12 100 Customer instances best solutions and average found by MPMA per instance: Con-
tinued . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.1 Summary of wind farms maintenance scheduling works, objectives, and types of models 98
5.2 Epsilon Constraints summary results for Froger et al. [31] instances . . . . . . . . . . 105
5.3 Summary of literature with complex models for maintenance strategy selection . . . . 110
5.4 Weibull parameters for components failures in Abdollahzadeh et al. [1] . . . . . . . . . 115
5.5 Costs, technicians, and time requirements per maintenance component based on Ab-
dollahzadeh et al. [1] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
A.1 Taxonomie des VRPs basée sur l'article de Pillac et al. [20] . . . . . . . . . . . . . . . 138
A.2 Synthèse des résultats sur le nouvel ensemble d'instances . . . . . . . . . . . . . . . . . 143
vii
A.3 Comparaison des diérentes MPMA Instances avec 100 clients . . . . . . . . . . . . 147
A.4 Comparaison du MPMA avec la métaheuristique ILS de Miranda et Conceição [18] . 147
A.5 Comparaison de MPMA avec la TS de Nguyen et al. [19] . . . . . . . . . . . . . . . . 148
viii
Chapter 1
Introduction
The world of science lives fairly comfortably with paradox. We know that light is a wave, and also
that light is a particle. The discoveries made in the innitely small world of particle physics
indicate randomness and chance, and I do not nd it any more dicult to live with the paradox of
a universe of randomness and chance and a universe of pattern and purpose than I do with light as
a wave and light as a particle. Living with contradiction is nothing new to the human being"
Madeleine L'Engle
The transport activities are one of the more important drivers in many of the economic activities.
In fact, according with the International Trade Administrator, logistics and transportation activities1 accounted for 8% of the Gross Domestic Product in the United States in 2015. Additionally, only
considering the logistic activities, transportation can represent up to 60% of the total costs [16],
making this topic an important area to study.
In the Operations Research context, the transportation problems and specically the Vehicle Rout-
ing Problems (VRPs) has been one of the most studied problems with more than a thousand published
papers between 1954 to 2006 [6]. Overall, the VRP consist of nding a set of minimal cost routes
performed by a set of vehicles, satisfying a set of clients, and respecting specic constraints, according
to the particular context. Furthermore, most of the works on this eld consider that all problem
parameters, travel and service times, and demands, are known in advance. Actually, Braekers et al.
[3] identify that more than 80% of 277 published articles between 2009 and 2015 in the VRPs context
are deterministic.
Many factors could aect the certitude of the information on problems parameters. For example,
the cities population concentration and their consequent trac congestion make the travel times un-
certain [11]. Furthermore, the accelerated use of Information Technologies could generate incertitude
about the requests because clients could made them more frequently and are subject to random ex-
ternal factors. Moreover, the time spent at each location visited by the vehicles can change because
of the complexity of the task to satisfy at the clients or due to the environmental conditions.
The consequence of neglecting the variability is that the solutions obtained could perform badly
in real uncertain environment [17]. Actually, based on a previously literature results, ignoring the
incertitude of the information could increase up to 10% the costs for the stochastic demands case
[8] or 4% for stochastic times [1]. Moreover, the amount of the objective (costs or utility) variation
between deterministic and stochastic solutions depends on the randomness of the problem and can be
as signicant as 20% when travel and service are uncertain [4].
In recent years, some authors have been working on stochastic VRPs, one of the most compre-
hensive study about this topic is given by Gendreau et al. [10]. The authors focus their attention
on the stochastic programming modeling which is the predominantly approach in the eld. Thus,
1Accessed the rst may 2018 - https://www.selectusa.gov/logistics-and-transportation-industry-united-states
1
only parameters which are dened by random variables or scenarios are considered2. Furthermore, to
classify the dierent problems the authors consider two main characteristics: the solution paradigm
and the stochastic parameters (customers, demands, and times). Solution paradigms are divided in
two: a priori and reoptimization paradigms. The former, consider that solutions are created before
any information is revealed (rst phase) and they are barely modied during the execution (second
phase). Meanwhile, reoptimization approach aims to modify the solution as new information becomes
available to improve. The latter approach is more related with the Dynamic problems (see Psaraftis et
al. [18] for a recent review). A priori paradigms can be further divided into Stochastic Problems with
Recourse (SPR) and Chance Constrained Problems (CCP). The SPR use recourse to react to failures,
or constraints violations during the second phase. Meanwhile, the CCP bounds the probability of
possible failures appearing during the second phase.
The a priori paradigm has been the predominant approach to solve stochastic VRPs, particularly
using SPRs. This can be explained by the fact that this type of models allow stable tactical routes
which are operationally desirable [9]. Moreover, according Bekta et al. [2], a priori paradigm is
suitable when anticipating uncertainty is crucial to nd feasible solutions and avoid penalties (economic
and reputation). Nevertheless, the a priori paradigm adds complexity of dealing with probability
calculus overhead. This makes that the size of stochastic problems that can be solved (exactly and
approximately) is rather small. For example, Gauvin et al. [8] are able to optimally solve only one
instance with 100 customers in the context of the VRP with stochastic demands. Therefore, there is
a need to develop solution methods able to tackle closer to real problems settings, in terms of size,
multiple uncertainties, and assumptions.
The literature review reveals that there is a lack of comparisons among dierent methods and
works between stochastic VRPs, which might be caused by the lack of standardized benchmarks.
Although each problem has its own characteristics (type of distribution, amount of variance, etc.)
base tests serve to prove the usefulness of new models and strategies to solve the stochastic VRPs.
Moreover, these test cases need to evolve, particularly in terms of size, to prove the capacity of the
new methods to deal with closer to life real problems. Being able to solve this type of problems will
demonstrate their capacity to fully exploit the benets of stochastic solutions over deterministic ones.
Last but not least, new stochastic models need to incorporate real life constraints, such as hard time
windows, a characteristic that has been largely studied in deterministic VRPs but not so much in the
stochastic ones.
This thesis addresses two kinds of VRPs using the stochastic programming framework and the a
priori paradigm: the rst one considers the demand as a random variable and is reported in chapter 3
where results for middle and large instances are reported, second it tackles in chapter 4 the stochasti-
city on the travel and service time. The latter under the presence of hard time windows which can
conduce to unserviced customers and using dierent types of continuous distribution for the stochastic
parameters. Both problems are presented in the context of maintenance operations for which anticipat-
ing uncertainty is imperative. The last problem studied in this thesis considers maintenance planning
on wind farms. It extends the stochastic VRPs in which technicians are to be scheduled to perform
their tasks under the appearance of uncertain new tasks (demands), random weather conditions, and
with stochastic service times. The present thesis is developed as follow:
Chapter 2 introduces an extensive review on the Vehicle Routing Problems (VRPs). Starting
with the description of deterministic VRPs, it makes its path to uncertain VRPs as their natural
evolution. Then, the attention is focused in the three main paradigms to model uncertain VRPs,
namely, Stochastic Optimization, Interval Optimization and Fuzzy Logic. Special consideration is
given to the Static Stochastic VRPs with a comprehensive review of the solution approaches and
dierent problems variants tackled in the literature. This revision shows that albeit the increase of
2Nevertheless, the parameters can be also modeled by sets (Robust Optimization) or by Fuzzy Variables. Moreinformation is given in chapter 2
2
CHAPTER 1. INTRODUCTION
research in the eld, there is still a lack of detailed results for big instances. Moreover, a lack of
research for the stochastic VRPs with hard time windows is established. Especially for the case with
stochastic travel and service times modelled by continuous random variables, so it is likely they are
in real applications.
Chapter 3 is devoted to the VRP with stochastic demands (VRPSD). In the VRPSD, customers
demands are modeled by random variables and their realization value are only known when the
vehicles arrive at the customers. A simple classical recourse action is used to model the VRPSD as
a stochastic problem with resource. To tackle the VRPSD a Greedy Randomized Adaptive Search
Procedure (GRASP) is used to restart a Memetic Algorithm (MA) and eciently solve the problem at
hand. In this chapter it is shown that large instances (up to 385 customers) can be eectively handled
by the MA+GRASP. A comparison with the state-of-the-art algorithms for the VRPSD shows that
the MA+GRASP provides better and more accurate solutions in very competitive computational
times. Moreover, the chapter establishes a new testbed of instances (based on instances already used
in deterministic context) with a higher number of customers than the traditional Christiansen and
Lysgard benchmark [5]. These results are important to open the space to discussion and further
design of other methods to VRPSD. The work presented in this chapter is under minor revision in
the Computers & Operations Research journal and an earlier version was presented at the CIE45
conference [12].
Chapter 4 focuses on a VRP with uncertain times. It presents a VRP considering stochastic travel
and service times with hard time windows. The problem is thought-out in a maintenance activities
context and the uncertainty in times are modeled through continuous probability distributions. A
model is proposed to enable the control of customers service levels but also considers the implica-
tions of missing the time windows. To overcome the problem of modeling the arrival times, it is
shown that they can be fairly approximated using a log normal distribution. To solve the problem, a
Multi-population Memetic Algorithm (MPMA) exploiting dierent characteristics in each population
(running in parallel) is proposed. Results are presented for instances with up to 100 customers derived
from the the Solomon [19] benchmark. Additionally, the MPMA is compared against state of the art
methods although these allow late services, and the proposed approach shows very good performance.
The results of this third chapter are gathered in a paper which is accepted for publication in the
Computers & Industrial Engineering journal. Preliminary results were presented at MIM2016 [14]
and CLAIO2016 [13] conferences.
Chapter 5 introduces a general review of the wind farms maintenance activities. Two related prob-
lems are further explored in this context. First, a multi-objective approach to deal with maintenance
scheduling of wind farms is addressed. In this problem the operator of the wind farm needs to decide
the order, time, and resources assignation to execute a set of maintenance tasks in a short term ho-
rizon. Moreover, the operator aims to minimize its costs while the investors want to maximize the
energy production. A linear integer model is used to model the problem and the epsilon-constraint
method is designed to approximate the optimal Pareto front. Tests are performed on the set of in-
stances proposed by Froger et al. [7] pointing out that objectives are in conict. Also it is shown
that the variation of energy production in the short term can be highly aected by the scheduling
of the activities. The second problem, extends the scheduling problem and explores the selection
of maintenance strategies for wind farms. Dierent strategies are evaluated within a event discrete
simulation approach to compare them on a long-term horizon. Furthermore, dierent ways of solving
the scheduling the maintenance tasks in the short term are compared within the simulation. Failures
appearances as well as maintenance times are considered as random variables. The proposed shows
that even simple heuristic rules are used to tackle the scheduling of technicians, they can have import-
ant eects on both the costs and the energy production. The results for the scheduling maintenance
activities problem considering multiple objectives were presented at IEOM 2017 conference held at
Bogota [15].
3
Finally the thesis ends with chapter 6 drawing the conclusions of the thesis and research clues for
future research on the uncertain vehicle routing problems, scheduling of maintenance activities, and
strategy selection in the wind farm context.
4
Bibliography
[1] N. Ando and E. Taniguchi. Travel time reliability in vehicle routing and scheduling with time
windows. Networks and Spatial Economics, 6(3):293311, 2006.
[2] T. Bektas, P. P. Repoussis, and C. D. Tarantilis. Chapter 11: Dynamic vehicle routing problems.
In Vehicle Routing: Problems, Methods, and Applications, Second Edition, pages 299347. SIAM,
2014.
[3] K. Braekers, K. Ramaekers, and I. V. Nieuwenhuyse. The vehicle routing problem: State of the
art classication and review. Computers & Industrial Engineering, 99:300 313, 2016.
[4] A. M. Campbell, M. Gendreau, and B. W. Thomas. The orienteering problem with stochastic
travel and service times. Annals of Operations Research, 186(1):6181, 2011.
[5] C. H. Christiansen and J. Lysgaard. A branch-and-price algorithm for the capacitated vehicle
routing problem with stochastic demands. Operations Research Letters, 35(6):773 781, 2007.
[6] B. Eksioglu, A. V. Vural, and A. Reisman. The vehicle routing problem: A taxonomic review.
Computers & Industrial Engineering, 57(4):1472 1483, 2009.
[7] A. Froger, M. Gendreau, J. E. Mendoza, E. Pinson, and L.-M. Rousseau. Solving a wind turbine
maintenance scheduling problem. Journal of Scheduling, 21(1):5376, 2018.
[8] C. Gauvin, G. Desaulniers, and M. Gendreau. A branch-cut-and-price algorithm for the vehicle
routing problem with stochastic demands. Computers & Operations Research, 50:141 153, 2014.
[9] M. Gendreau, O. Jabali, and W. Rei. 50th anniversary invited articlefuture research directions
in stochastic vehicle routing. Transportation Science, 50(4):11631173, 2016.
[10] M. Gendreau, O. Jabali, W. Rei, P. Toth, and D. Vigo. Stochastic vehicle routing problems.
Vehicle Routing: Problems, Methods, and Applications, 18:213, 2014.
[11] A. R. Güner, A. Murat, and R. B. Chinnam. Dynamic routing under recurrent and non-recurrent
congestion using real-time its information. Computers & Operations Research, 39(2):358 373,
2012.
[12] A. Gutierrez, L. Dieulle, N. Labadie, and N. Velasco. A memetic algorithm for the vehicle
routing problem with stochastic demands. In Proceedings of the CIE 45 International Conference
on Computers and Industrial Engineering, 2015.
[13] A. Gutierrez, L. Dieulle, N. Labadie, and N. Velasco. An approximate column generation for the
vehicle routing problem with hard time windows and stochastic travel and service times. 2016.
[14] A. Gutierrez, L. Dieulle, N. Labadie, and N. Velasco. A multi population memetic algorithm
for the vehicle routing problem with time windows and stochastic travel and service times. In
Proceedings of the 8th IFAC Conference on Manufacturing Modelling, Management & Control,
2016.
5
BIBLIOGRAPHY
[15] A. Gutierrez, L. Dieulle, N. Labadie, and N. Velasco. Wind farm maintenance scheduling model
and solution approach. In Proceedings of the International Conference on Industrial Engineering
and Operations Management, 2017.
[16] M. Hesse and J.-P. Rodrigue. The transport geography of logistics and freight distribution.
Journal of Transport Geography, 12(3):171 184, 2004.
[17] F. Louveaux. An Introduction to Stochastic Transportation Models, pages 244263. Springer
Berlin Heidelberg, Berlin, Heidelberg, 1998.
[18] P. H. N., W. Min, and K. C. A. Dynamic vehicle routing problems: Three decades and counting.
Networks, 67(1):331.
[19] M. M. Solomon. Algorithms for the vehicle routing and scheduling problems with time window
constraints. Operations Research, 35(2):254265, 1987.
6
Chapter 2
Literature Review - Vehicle Routing
Problems
Nearly 60 years have passed since the introduction of the vehicle routing problem (VRP). The rst
literature appearance of the VRP can be traced back to the seminal work of Dantzig and Ramser
[41] named The Truck Dispatching Problem. Since 1959, the number of articles and applications
have grown tremendously. Since that year, a general search for the Vehicle Routing Problem in a
searching engine such as Google Scholar gives more than 20.000 results that can be reduced to over
4600 if the words are present in the document title. Eksioglu et al. [49] reviewed nearly 1500 VRP
references from 1954 to 20061, which included journal articles, books, book chapters, technical reports,
and conference articles. The authors proposed a taxonomy to classify the vast literature and asserted
that it grew exponentially at a rate of approximately six percent per year. A more recent classication
work can be found in Braekers et al. [30] where 277 VRP journal articles from 2009 to mid-2015 were
arranged using a taxonomy similar to the one used in Eksioglu et al. [49].
The massive amount of research related to the VRP can be twofold explained. First, transportation
plays a central part in many human activities, economics, and the environment. According to Hesse
and Rodrigue [82] transportation accounted for nearly 6% of the Gross Domestic Product (GDP)
of the United States in the year 2000. Moreover, transportation transcends the purely economic
trend. In fact, the subject has been studied in the context of disaster relief and humanitarian logistics
([74, 34]), and services delivery (health care [55], technicians [147], among other). Second, the eld
has been the seed for many developments of several exact and heuristic methods for combinatorial
optimization problems [102]. These developments have an impact in other elds in the Operational
Research community [84] and therefore make the VRP research an active and important part of the
scientic development.
Besides, the nearly sexagenarian problem has seen a myriad of variants and extensions of its
basic version. Either by the addition of more characteristics, constraints, or changes on the objective
function, new problems have risen to adapt the VRP to many contexts. Within this variety, the last
few decades have seen an increment in the study of problems where the parameters information is not
certain [63]. Beyond the pure theoretical value of the works, applications deal with a reality in which
information is far from perfect and stochastic (weather, accidents, drivers skills, etc.). Moreover,
despite the usual necessary eort to solve problems with uncertain information, its value is not trivial
[6, 33, 60]. Therefore, the uncertain VRPs are an important eld to make both theoretical and
practical research.
This chapter presents a review of the VRP. It starts by introducing one of the most basic and
known version of the problem, the Capacitated Vehicle Routing Problem (CVRP). The CVRP serves
1The authors use the date 1954 as the rst VRP record in the literature considering the work of Dantzig et al. [40]on the Traveling Salesman Problem (TSP), a particular case of the VRP
7
2.1. DETERMINISTIC VRP
as proxy to introduce the mathematical formulations and a very special generalization called the VRP
with Time Windows (VRPTW). Then, the uncertain VRPs modeling and solution approaches are
explored. The chapter ends with concluding remarks on the importance of stochastic VRPs.
2.1 Deterministic VRP
2.1.1 Capacitated Vehicle Routing Problem
In its basic form, the Capacitated Vehicle Routing Problem (CVRP) can be dened by a complete
undirected graph G = (V,E) where V = 0, 1, . . . , i, . . . , n and E = [i, j]∀i, j ∈ V | i < j arethe vertex and the edge sets respectively. Moreover, let V c = V \ 0 be the customers subset. Eachcustomer has a non-negative demand qi. Vertex 0 is a depot where is located a set of homogeneous
vehicles with limited capacity Q. Furthermore, each edge [i, j] ∈ E has a non-negative cost cij . The
objective of the CVRP is to build a set of routes with minimum cost considering that each route
starts and ends at the depot, the vehicle capacity Q must be respected, and no split deliveries are
allowed. Moreover, a generic route r is dened as an ordered sequence of nodes r = r0, r1, . . . , rj ,. . . , rk, rk+1 where rj represents the jth visited node. Each vehicle starts and ends its route at the
depot, therefore, r0 = rk+1 = 0 for every route. Even more, each route r has an associated cost
Cr =∑kj=0 crj ,rj+1 .
Several other extensions and variants for the CVRP have been proposed aiming to bring the models
closer to real life applications. Among these, one can nd the Distance Constrained VRP (DVRP)
which limits the total distance traveled by each vehicle to a threshold [19, 5]; the Heterogeneous VRP
(HVRP) where the eet of vehicles is, as its name states, heterogeneous (in terms of capacity or costs)
[7, 117, 138]; the Multi Depot VRP (MDVRP) which considers multiple depots where the vehicles start
and end their routes[146, 172]; the Periodic VRP (PVRP) that requires repeated visits to customers
[57]; the open VRP (OVRP) which does not require vehicles to return to depot after serving the last
customer [116, 117]; the Orienteering Problem (OP) where customers have an associated prot (or
score) collected by a xed size eet not necessarily sucient to visit all the customers, and the objective
is to maximize the total prot [170, 98, 80]. Other variants include more additional constraints such as
the Pickup and Delivery Problems (PDP) where people or goods must be transported from dierent
origins to dierent destinations [135, 136, 17, 18]. The reader is referred to the mentioned bibliography
and to Toth and Vigo [169] for further details on variants of the CVRP.
CVRP Mathematical formulations
Three formulations are mainly used to model the CVRP [154, 101], the vehicle ow, the commod-
ity ow and the set partitioning formulations. The vehicle ow formulation uses integer variables
xij ∀i, j ∈ V to represent the number of times an edge is used in the optimal solution [107, 108].
Model M1CVRP presents a classical two-index network formulation.
M1CV RP : min∑i,j∈V
xijcij (2.1)
∑j∈V c
x0j = 2m (2.2)
∑i<p|i∈V
xip +∑
j>p|j∈V
xpj = 2 ∀p ∈ V c (2.3)
∑i∈S,j /∈Sor
i/∈S,j∈S
xij ≥ 2b(S) ∀S ⊂ V c (2.4)
8
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
xij ∈ 0, 1 ∀i, j ∈ V c (2.5)
x0j ∈ 0, 1, 2 ∀j ∈ V c (2.6)
In M1CVRP the objective (2.1) minimizes the total costs associated with the used edges. Con-
straint (2.2) determines the degree of the depot, using m as the number of vehicles (m can be a
variable). Constraint (2.3) ensures that any vehicle visiting a customer must leave to another node.
Constraint (2.4) serves to guarantee capacity restrictions as well as to prevent subtours formation,
that is, ensemble of connected customers without being linked to the depot. Practically, the term b(S)
can be set to⌈∑
i∈SqiQ
⌉, therefore, b(S) is a lower bound on the number of vehicles needed to satisfy
the demand of the subset of customers S. Furthermore, constraints (2.5) and (2.6) stand for variables
nature.
The second formulation, called commodity ow formulation makes use of continuous variables to
model the amount of vehicle load and empty space on the vehicle, when an edge is used. The reader is
referred to Baldacci et al. [9] for a complete formulation. The third formulation is the set partitioning
one. This one relies on the enumeration of all feasible routes which are then selected through a
set partitioning problem [11]. Associated to each route a binary variable serves to decide if it is
included within the solution or not. The reader is referred to Laporte [101] for the complete model.
Other formulations beside the three described can be used. For instance, three-index formulations
add an index to identify each vehicle separately. This types of models are useful when particular
characteristics of each route aect its feasibility or cost. One of the most common VRP extension
modeled by three-index formulation is the one with Time Windows (VRPTW), where time constraints
are imposed for the customers visits. It is now explored in more depth.
2.1.2 VRP with Time Windows
The Vehicle Routing Problem with Time Windows (VRPTW) is one of the most important and well-
studied VRPs. Usually the VRPTW uses an extended graph G =(V ,A
). The vertex set V includes
an exact copy of the depot node called n+1, therefore, V = V ∪n+ 1. Moreover A stands for the arcs
set dened as as A = (i, j)∀i, j ∈ V | i 6= j. In addition, each arc (i, j) takes a time tij ∀ (i, j) ∈ Ato be traversed. Also, each customer requires a time ti ∀ V c to be served.
The VRPTW extends the CVRP by dening a time window [ei, li]∀i ∈ V c. The vehicle must
start to service the customer during this lapse of time. Time windows constraints can be dened
as hard or soft [43]. In the hard version, the vehicles cannot start their services outside the time
windows. Nevertheless, early arrivals, i.e. arriving before ei, are possible but vehicles must wait until
the opening of the time window. Soft version allows services outside the time window at the expense
of a penalization cost. Besides, the depot often has a time window [e0, l0] representing the earliest
departure time and the latest arrival time for vehicles to the depot. The time window is the same for
the depot copy n + 1, i.e. [en+1, ln+1] = [e0, l0]. These additional constraints on service start times
add another layer of complexity when compared to the classical CVRP. When the number of vehicles
is xed, even computing a feasible solution is NP-Hard [150]. The objective of the VRPTW can dier
from that of the CVRP. According to Desaulniers et al. [43] exact approaches to the VRPTW usually
consider the same objective function as in the CVRP, i.e. the total cost of the routes. Meanwhile,
heuristic and metaheuristic methods are often designed to rst minimize the number of required
vehicles then the total cost in a hierarchical way.
Mathematical formulations
Similar to the CVRP there exist many formulations for the VRPTW. M2VRPTW presents a three-
index formulation for the version with hard time windows. In this, the binary variable xijl ∀ i, j ∈V , l ∈ L takes value one if the vehicle l uses the arc (i, j). Besides, Til stands for the time when
9
2.1. DETERMINISTIC VRP
the service starts at customer i ∈ V c by vehicle l. Model M2VRPTW minimizes (2.7) the total cost
assuming a xed eet of vehicles.
M2V RPTW : min∑l∈L
∑i,j∈V
xijlcij (2.7)
∑l∈L
∑j∈V
xijl = 1 ∀i ∈ V c (2.8)
∑j∈V
x0jl = 1 ∀l ∈ L (2.9)
∑i∈V
xijl −∑i∈V
xjil = 0 ∀j ∈ V c, l ∈ L (2.10)
∑i∈V c∪0
xi,n+1,l = 1 ∀l ∈ L (2.11)
T il + ti + tij − T jl ≤M (1− xijl) ∀l ∈ L, (i, j) ∈ A (2.12)
ei ≤ T il ≤ li ∀l ∈ L, i ∈ V (2.13)∑i∈V c
qi∑j∈V
xijl ≤ Q ∀l ∈ L (2.14)
xijl ∈ 0, 1 ∀ (i, j) ∈ A, l ∈ L (2.15)
T il ∈ <+ ∀i ∈ V , l ∈ L (2.16)
Constraint (2.8) guarantees that each customer is served by only one route. Meanwhile, constraint
(2.9) species that all vehicles must leave the depot. In addition, constraint (2.10) guarantees that
a vehicle visiting a customer must leave to another node. Moreover, constraint (2.11) states that all
vehicles nish their route at node n+ 1. Note that in this formulation, the number of eectively used
vehicles can be less than |L| as far as variable x0,n+1,l can take value 1. Furthermore, constraints (2.12)
to (2.13) ensure that the times when services start, respect the nodes time windows. In constraint
(2.12) term M is a large value that let the equation holds when xijl takes value zero. Vehicles
capacity constraint is guaranteed by (2.14). Last but not least, constraints (2.15) and (2.16) stand for
the variables nature.
2.1.3 Solution Methods
Solving the VRPs is not an easy task, since the CVRP is an NP-Hard problem and most of its variants
are NP-Hard as well, including the uncertain VRPs. Solution approaches can be classied according
to the nature of the solution. In this vein, exact methods guarantee that the optimal solution will be
found but at the expense of a prohibitive running time even for medium size instances. Approximate
methods on the other hand usually provide quickly a solution but this last can be not optimal. Figure
2.1 provides a simple scheme to VRP solutions approaches. The scheme is not exhaustive but allows
to navigate through the extensive amount of methods.
10
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
Solution approaches for VRPs
Exact methods
Heuristics Metaheuristics
Constructive
Two-phase
Branch and Price
Branch and Price and Cut
Branch and Cut
Tabu Search
Simulated Annealing
Iterated Local Search
Matheuristics
Clarke & Wright
Nearest Neighborhood
Ant Colony Optimization
Genetic Algorithms
Cluster-first, route second
Route-first, cluster second
Approximate methods
Figure 2.1: Solution approaches scheme.
Exact methods
Exact approaches for the VRPs are highly related to the way the problem is modeled. Dierent
methods have been used for solving the CVRP and the VRPTW such as the Branch-and-price (BP),
Branch-and-cut (BC) and Branch-and-price-and-cut (BPC) [58, 43]. BC has been mainly based on
the two-index formulation (M1CVRP) or an analogous version for the VRPTW [12, 90]. Overall,
the BC works by relaxing integrality constraints and discarding the set of constraints represented by
(2.4) for the CVRP and (2.12) to (2.14) for the VRPTW. The BC solves the relaxed problem and
identies any subset of variables that violates the removed constraints. If this set is found, it generates
the violated constraints, add them to the problem and reiterates. Moreover, when no constraints are
identied, the BC branches on a fractional variable and creates problems that are solved with the
same approach.
BPC works similarly to BC methods. The dierence relies in the fact that each subproblem
(usually the Shortest Path Problem with Resource Constraints) relaxation is solved by means of a
column-generation approach. This last approach exploits the set covering formulation for both the
CVRP and the VRPTW. Dierent versions of BPC algorithms exist since dierent approaches can
be used to solve the subproblems (e.g. ng-routes, q-routes, bidirectional search), or the types of cuts
(constraints) added during the iterations. BPC has shown to be the state-of-the-art to solve the CVRP
[8, 58, 137] as well as the VRPTW [95, 42, 87]. Nevertheless, Baldacci et al. [10] has proposed the
best method based on a reduced set partitioning for the VRPTW. Further analysis and description
of the methods are available in [89, 43].
Approximate methods - Heuristic and Metaheuristics
While exact methods have seen an incredible development in the last years, the combinatorial nature
of the VRPs limit their use to relatively small instances. Nowadays, for example the CVRP can be
consistently solved for problems with up to 200 customers [137]. Meanwhile, VRPTW is consistently
solved for instances with up to 100 customers [10]. Still, since applications can easily overpass this size,
heuristics and metaheuristics are omnipresent in the literature. Heuristics are approximate algorithms
which try to nd good solutions in competitive running times.
Laporte and Semet [110] classify heuristics under constructive and two-phase methods. Labadie
et al. [99] also follow this classication. In general words, constructive heuristics work by creating an
initial solution that can be further improved [109]. Clarke and Wright savings [39] is by far the most
11
2.1. DETERMINISTIC VRP
known heuristic due to its simplicity [110]. The heuristic works by iteratively merging pairs of routes
into single ones, provided that this implies a saving and guarantees feasibility. The process is repeated
until no further merges are feasible or the maximum possible saving is negative. Further information
on basic heuristics can be found in Toth and Vigo [168].
Several constructive heuristics tailored for the VRPTW are introduced by Solomon [157]. By far
the most important and successful is the insertion heuristic called I1. Iteratively I1 creates routes
starting with some seed customers. These are selected from dierent rules, such as the farthest not
visited customer or the one with the earliest initial time window. Once a seed has been selected, I1
calculates an insertion (of non visited customers) criteria based on the classical savings (distance) and
the extra time required by adding the new customer. The best customer not yet visited is added in
the best possible position. The algorithm iterates until no customers can be added to the current
route, then it starts a new one until all customers are visited. The reader is referred to Bräysy and
Gendreau [31] for other constructive approaches.
Two-phase methods can be further divided into cluster-rst, route second and route-rst, cluster
second. The rst one is based on the idea of creating groups or clusters of customers respecting
the capacity constraint. Then, customers in the cluster are ordered to completely dene a route, by
solving a Traveling Salesman Problem (TSP) for each group. Several approaches can be used to create
clusters: the sweep algorithm [70] uses angular sectors from the depot to create the necessary clusters.
Fisher and Jaikumar heuristic [56] also uses the idea of clusters around seeds aiming to minimize the
distance from customers to cluster seeds.
The route-rst, cluster second approach is mainly based on the idea of creating a giant tour (TSP
tour) without considering capacity or other constraints, and then splitting it into feasible routes. This
approach was introduced by Beasley [14] and has received more attention since the work of Prins
[140]. Indeed, Prins showed that route-rst, cluster second algorithms could be as ecient as methods
relying on classical methods at the date, such as the Tabu Search. Further examples on the route-
rst, cluster second can be found in Labadie et al. [97] with an application to the VRPTW, Prins et
al. [141] addressing the Capacitated Arc Routing Problem and the CVRP, Mendoza et al. [125] in
a multi-compartment vehicles with uncertain parameters problem, Velasco et al. [171] with a multi
objetive pick-up and delivery problem, and Mendoza et al. [126] in the context of a CVRP with
stochastic demands. A recent review on on the route-rst, cluster second approach can be found in
Prins et al. [142].
Although heuristics commonly provide a good trade-o between eciency and quality, they are
usually coupled with local search or improvement procedures. The underlying concept of local search
is the denition of neighborhoods [31, 99]. These lasts are considered as close related solutions to a
generic solution s. Neighborhoods are structured in a way such that movements can be performed
on s to achieve a new solution s′. Usually, this type of procedures contain two types of movements,
namely intra-route and inter-route ones [102]. The rst one aects only one route trying to improve
it while the second considers and changes more than one route. Moreover, neighborhoods can be
wholly explored to select the best improving movement (best acceptance) or partially explored until a
movement improves the current solution (rst acceptance) [133]. The exploration of neighborhoods is
performed in an iterative way, until reaching a stopping condition or achieving a local (global) optimal
solution. Among the most used neighborhoods one can nd the k-opt movements [119] for which 2-Opt
and 3-Opt are the most popular cases, the b-cyclic, k-transfer scheme [166], Or-opt movements [132]
and λ−interchange movements [133]. More complex movements can also be found in the literature,
e.g. GENI exchange [62] and ejection chains [144, 73]. For further details on these neighborhoods
and their characteristics, the interested reader is referred to [59, 31]. Furthermore, details on ecient
implementations of evaluation tests allowing to know either these movements are feasible or not, and
to compute the extra cost generated, can be found in [93, 173].
Despite the success of local search procedures, they are often trapped in local optima when they
12
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
are applied to only one initial solution obtained with a constructive heuristic. To overcome these
problems, metaheuristics are a good option. According to Bianchi et al. [24] metaheuristics are high
level procedures that combine heuristics in a more general framework. Osman and Laporte [134]
dened them as A metaheuristic is formally dened as an iterative generation process which guides
a subordinate heuristic by combining intelligently dierent concepts for exploring and exploiting the
search space, learning strategies are used to structure information in order to nd eciently near-
optimal solutions. Originally, metaheuristics were easily identied and dierentiated, however, the
increasing hybridization of such methods have blurred the lines between them [109, 29].
Still, according to Laporte et al. [109] metaheuristics can be classied into trajectory and population-
based methods. In the rst one, a solution move to another by searching in a neighborhood. Mean-
while, population-based methods use a set of solutions that interact to improve the solution quality.
Within the rst category classication one can nd methods such as the Simulated Annealing (SA)
[94], Tabu Search (TS) [72, 68], Variable Neighborhood Search (VNS) and Variable Neighborhood
Descent (VND) [129], Deterministic Annealing (DA) [47, 115], Iterated Local Search (ILS) [13, 120].
Population-based metaheuristics include Genetic Algorithms (GAs) [143, 140], Ant Colony Optimiz-
ation (ACO) [145] and Scatter Search (SS) [71].
Another important trend in solution methods for the VRP are the matheuristics [44, 123]. Math-
euristics work by hybridizing heuristics (or metaheuristics) and exact algorithms such as Integer
Programming (IP). These methods cooperate and share information to improve the solution quality.
An interesting approach is the Petal heuristics which make use of the set partitioning model. In this
one, promising routes (often called petals [102, 99] in this context) are added to a set partitioning
problem as it is done in a column generation scheme [11, 149, 146]. A similar approach is used by
Mendoza et al. [127, 126], by constructing several solutions from constructive heuristics and then
solving a set partitioning problem. Further information on solution methods for dierent variants of
VRP can be found at [28, 67, 32, 69, 99].
2.2 VRP under uncertainties
A majority of the studies on the dierent VRP variants have been carried out under the assumption
that all relevant information is known with certitude when solving the problems. That is, problems
are solved in a deterministic static way [139, 30]. Nevertheless, dealing with real life applications
implies the appearance of uncertainties. These lasts arise from many reasons, e.g. weather conditions,
accidents, customer presence, etc. Neglecting the uncertainties is not always an option since the eects
of variability can have important consequences on solutions quality. Actually, it has been shown
that deterministic solutions2 can lead to systematically bad solutions in an uncertain environment
[121, 158].
Talking about uncertainties is a discussion highly related with information. Certainly, the quality
of information and the times when it is available play a major role in both, the models and solution
approaches for this type of problems. Moreover, what can be known about the uncertain parameters
denes the framework in which an uncertain VRP can be tackled. Pillac et al. [139] dene four
categories due to sub levels of information characteristics, i.e. information evolution and information
quality. Their taxonomy is presented in table 2.1 by considering general uncertain inputs.
2Based for example on the expected value of the variable parameters
13
2.2. VRP UNDER UNCERTAINTIES
Table 2.1: VRPs taxonomy based on Pillac et al. [139] - Information evolution and qualityInformation QualityDeterministic input Uncertain inputs
Informationevolution
Input knownbeforehand
Static and deterministic Static and uncertain
Input changesover time
Dynamic anddeterministic
Dynamic and uncertain
The top-left box stands for Static and deterministic problems. This category represents the prob-
lems where all parameters are known beforehand, and their values are certain. Moreover, once the
solution is deducted it remains unchanged as far as no new information arise. The CVRP and VRPTW
lie within this category. In Dynamic and deterministic problems, part or all the inputs is revealed
dynamically during the execution of the routes [139]. More importantly, there is no exploitable in-
formation about the dynamic parameters. Therefore, solutions are construct in an online way.
Static and dynamic uncertain problems (top-right and bottom-right boxes) share the fact that at
least one input (parameter) is not known for sure. Same as the dynamic deterministic case, parameters
true value is revealed at some specic moments, e.g. when the vehicle arrives at the customer.
Nevertheless, in uncertain problems there exist exploitable information about the parameter such as
its probability distribution, the interval set for its value, moments of the distribution, etc. Therefore,
static and dynamic uncertain problems can exploit this information to devise their solutions. There
are two main dierences between static and dynamic uncertain problems. First, in static problems
all inputs (even uncertain ones) are dened before solving the problem while this is not the case in
dynamic ones. Second, both categories dier in how solutions are created and treated. In the static
uncertain problems, solutions are created before the realization of the uncertain parameters and are
barely modied. Meanwhile, dynamic problems construct and change the solution as new information
arise.
The framework to handle the uncertainties (static or dynamic) clearly depends on the type of
problem tackled and how the information evolves. Additionally, how the uncertainties are modeled
plays a major role in the approaches to solve uncertain VRPs. Three main approaches have been used
to deal with uncertainties in VRPs, namely, stochastic, robust optimization, and Fuzzy logic. They
are now explored in further detail.
2.2.1 Stochastic VRP
Stochastic programming has been the main paradigm to deal with uncertain VRPs, driving the devel-
opment of what is known as Stochastic Vehicle Routing Problems (SVRP) [64]. The reader is referred
to the work of Birge and Louveaux [27] as a good entry point to the stochastic programming eld.
The main characteristics of SVRPs is the fact that uncertainty alters the condence on the problems
parameters, which are modeled as random variables. Thus, in static SVRPs, information is assumed
to be prior available to characterize the random parameters of the problem i.e. the probability dis-
tributions of the random parameters. As long as parameters are not certain, some constraints might
not be fully satised. Whenever a constraint is violated (due to the variability of the parameters) a
failure occurs.
SVRPs can be tracked down to the pioneer work of Tillman [167], who deals with the VRP with
stochastic demands (VRPSD) considering multiple depots. The VRPSD is an extension of the classical
CVRP where demands are modeled as random variables. Due to this fact, failures can take place
whenever a customer' demand is higher than the available remaining capacity in the vehicle. Aiming
to overcome and solve the SVRPs, two main paradigms have been mainly used [63]: the a priori
(static) and reoptimization (dynamic) paradigms.
14
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
The a priori optimization works by constructing a solution before the realization of the random
variables [86, 63]. Therefore, a priori paradigm can be viewed as a two-stage approach. After the
prior solution is built (rst-stage) the random parameters are revealed (second-stage). The way in
which this information is available depends on the problem. For example, many VRPSD formulations
[38, 76, 60, 126] assume that the customer demand is only known when the vehicle arrives at the
customer location. Besides, two main approaches are commonly used to optimize the a priori SVRPs,
the Stochastic Problem with Recourse (SPR) and the Chance Constraint Programming (CCP). SPR
use recourse which can be dened as policies or rules and actions that adjust the prior solution to deal
with specic situations such as failures in the second stage. Indeed, recourse can be used to respond to
failures. For example, the so called classical VRPSD recourse consists in a return to the depot to load
(unload) when a customer demand exceeds the current capacity of the vehicle and restarts the route
from this customer. It shall be noticed that recourse actions usually generate a cost which is properly
considered in the objective function. Thus, the SPR minimizes the rst-stage cost of the solution plus
the expected cost of the recourse (second-stage). CCP works by bounding the probability of failure to
a threshold. CCP can be used when recourse can be hardly dened [160] or to guarantee a service level
[118, 52, 178]. A CCP and SPR formulations for the VRPSD are introduced in the next sub-section
2.2.1.
The reoptimization paradigm on the other hand does not rely on a prior (static) solution. Con-
versely, it constructs and changes the solution as new information arises (dynamic). The increasing
amount and development of Information and Communication Technologies (ICT) has enabled to tackle
and evaluate problems in such a dynamic way [139]. Although this paradigm usually improves the
solutions quality when compared to its static counterpart, it also poses new challenges in terms of
computational eciency at operational level. Indeed, the speed in which solutions must be computed
is a limitation on such methods. Besides, a priori (static) approaches conduce to stable tactical routes,
which are operationally desirable [63]. Moreover, the a priori approach is preferable when anticip-
ating uncertainties is important for routes feasibility and to avoid costs (economic and reputational)
[15]. The main focus of the SVRPs is the static a priori approach, further information on dynamic
stochastic problems can be found at Pillac et al. [139], Bektaet al. [15], and Psaraftis et al. [130].
Given the static and stochastic context, the SVRPs literature is frequently classied with respect
to the parameters stochasticity [64]. The most common studied versions are the VRP with stochastic
demands, with stochastic customers, and with stochastic times. They are now further explored.
VRP with stochastic demands
Among the SVRP, the VRP with stochastic demands (VRPSD) is the furthermost studied problem.
The rst reported solution method for the VRPSD is proposed by Tillman [167] who used a modi-
cation of the Clarke and Wright heuristic [39]. The general VRPSD with recourse is described in
the model M3VRPSD while the CCP formulation is presented in model M5VRPSD. Both models are
proxies for the SVRPs. The notation of Gendreau et al. [64] is used. Dierences among M3VRPSD
and M1CVRP are the inclusion of the expected recourse cost (Q (x)) in objective function (2.1) and
constraint (2.4) which need to consider the demand expected value. Since demands are modeled as
random variables, they become qi∀i ∈ V c with expected value E [qi] = µi ∀i ∈ V c. The expected
recourse cost is determined by Q (x) [106].
M3V RPSD min∑i,j∈V
xijcij +Q (x, qi) (2.17)
∑i,j∈S
xij ≤ |S| −
⌈∑i∈S
µiQ
⌉∀S ⊂ V c, 3 ≤ |S| ≤ n− 1 (2.18)
(2.2)− (2.3) , (2.5)− (2.6) (2.19)
15
2.2. VRP UNDER UNCERTAINTIES
The objective function (2.17) minimizes the total travel costs plus the expected recourse costs.
Constraint (2.18) is equivalent to constraint (2.4), but the VRPSD version uses the expected value of
demands. M4VRPSD extends the M1CVRP but constrains the probability of a route failing at least
once to a level β. In this model, it is assumed that Vβ (S) is the minimum integer value which satises
that the subset S ⊂ V has a probability of failure not exceeding β. M4VRPSD minimizes the total
travel costs (2.20) while the routes probability of failure constraint is handled by (2.21). M3VRPSD
and M4VRPSD present the SPRs and CCPs models. The rst one considers the expected cost of
recourse (2.17), while the second one bounds failures probability (2.21).
M4V RPSD min∑i,j∈V
xijcij (2.20)
∑i∈S,j /∈Sor
i/∈S,j∈S
xij ≥ 2Vβ (S) ∀S ⊂ V c, 3 ≤ |S| ≤ n− 1 (2.21)
(2.2)− (2.3) , (2.5)− (2.6) (2.22)
SPR has been the dominant trend in the VRPSD (Bertsimas [22], Gendreau et al. [65, 66], Hjorring
and Holt [83], Christiansen and Lysgaard [38], Goodson et al. [76], Gauvin et al. [60], Mendoza et al.
[126]) when compared to the CCP formulation (Stewart and Golden [159, 160], Dror et al. [46]). The
most used recourse policy, the classical recourse is dened as follows. When the load of the vehicle is
fullled it returns to the depot to unload the charge, and resumes its assigned route from the failure
point [22]. However, other recourse policies have been studied and implemented through several
studies: preventive restocking policies are extensions of the classical recourse where return trips to the
depot are performed even if the vehicle is not empty to avoid future failures [176, 23, 25, 122, 177];
pairing strategies allowing the cooperation of multiple vehicles [3]; split deliveries between paired
routes [113] in which some customers are served by two vehicles; and backup routes [50] that receive
customers from primary routes. Although the use of more complex recourse policies can generate a
signicant saving relative to simpler ones [3], the latter have been preferred since they allow more
tractable models and stable tactical routes [63].
16
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
Table2.2:
Summaryof
VRPSD
literature
Author(s)
Typeofmodel
Recourse
Additionalconsiderations
Solutionapproach
Stew
artandGolden[160]
SPRandCCP
Classicalrecourse
Normaldistributeddemands,
independent
andcorrelated
ClarkeandWright[39]heuristic
DrorandTrudeau
[46]
SPRandCCP
Classicalrecourse
Direction
oftheroutes
impact
onobjectivefunction
ClarkeandWright[39]heuristic
Bertsimas
[22]
SPR
Classicalrecourse
andSkipping
custom
ers
Closed-form
expressionsfor
recourse
cost
Cyclic
heuristic
Grendeauet
al.[65]
SPR
Classicalrecourse
Stochasticcustom
ers
L-shaped
method
Hjorringet
al.[83]
SPR
Classicalrecourse
considering
exactstockout
Singlevehiclecase
L-shaped
Yanget
al.[176]
SPR
Optimalrestocking
policy
Route
duration
constraints
Route-rst-Cluster-Nextand
Cluster-First-Route-Next
heuristics
Bianchi
etal.[23]
SPR
Optimalrestocking
policy
VRPSD
andTSP
representations
Severalmetaheuristics
AkandErera
[3]
SPR
Pairedlocally
coordinated
Coordinationof
theeet
TabuSearch
ChristiansenandLysgaard
[38]
SPR
Classicalrecourse
Branch-and-price
Erera
etal.[50]
Mixed
CCPandSP
RPrimaryandback
uproutes
Hardtimewindows
HeuristicwithMonte
Carlo
simulation
Erera
etal.[51]
SPR
Classicalrecourse
andvariant
Route
duration
constraints
TabuSearch
Mendoza
etal.[125]
SPR
Classicalrecourse
Multi-com
partmentcase
Mem
eticAlgorithm
Goodson
etal.[76]
SPR
Classicalrecourse
Cyclic-Order
representation
for
solutions
Simulated
Annealing
Gauvinet
al.[60]
SPR
Classicalrecourse
TabuSearch
andbidirectional
labelalgorithm
Branch-and-cut-and-price
Mendoza
etal.[126]
SPRandCPP
Classicalrecourse
Route
duration
constraints
HybridGrasp
andheuristic
concentration
Nguyenet
al.[131]
SPR
Classicalrecourse
Hardtimewindowsand
Satiscing
Measure
Approach
TabuSearch
Luo
etal.[122]
SPR
Dynam
icrecourse
strategy
Weight-relatedcosts
AdaptiveLarge
Neighborhood
Search
17
2.2. VRP UNDER UNCERTAINTIES
After the VRPSD introduction, it has been widely studied. Table 2.2 shows a summary of the
literature of VRPSD. Exact methods have been used to solve the VRPSD, for instance Gendreau
et al. [65] employed an integer formulation mixed with the L-shaped method [104] to optimally
solve instances with up to 70 customers given that all customers are present. Still, their method is
restricted to discrete probability distributions [65]. Hjorring and Holt [83] tackle the VRPSD with
only one route. The classical recourse is used considering two scenarios: the rst one is the normal
stockout which represents the case when the vehicle returns to the depot and back to the customer
where the failure takes place. The second one is the exact stockout, representing the case when the
vehicle has just enough capacity to serve the current customer. In that case, the vehicle returns to
the depot and continues towards the next customer. The authors use an L-shaped approach to solve
the problem. Furthermore, the authors propose new optimality cuts that improve the performance of
the algorithm.
An important development in the eld is presented by Christiansen and Lysgaard [38]. The
authors proposed the rst branch-and-price algorithm to solve the VRPSD with classical recourse.
Moreover, they determined a testbed set of 40 instances based on Augerat sets A and P. The results
allowed to have a common benchmark. Moreover, although the method solved problems with up to
60 customers, it showed to be more powerful with a higher number of vehicles when compared to the
L-shaped method [65, 83, 106]. Gauvin et al. [60] developed an improvement of Christiansen and
Lysgaard work introducing a Branch-and-cut-and-price. The column generation is accelerated using
a TS heuristic and a bidirectional labeling algorithm. The BCP outperforms the BP of Christiansen
and Lysgaard [38]. In fact, 20 more instances are solved to optimally, so 38 of 40 instances are closed.
Furthermore, the method achieves to solve problems with up to 100 customers in less than 20 minutes.
Both works assume independent Poisson distributions. More recently, Biesinger et al. [25] propose to
use the L-shaped algorithm for the Generalized VRP with stochastic demands. More interesting is
that the recourse action corresponds to a preventive restocking policy [176] in contrast to the classical
one. Therefore, this is is the rst exact method that considers such a dierent recourse. Results are
presented for small size instances with dierent types of variations. The solution method is proven
to be eective for instances involving up to 40 nodes and with less than three expected restocking
actions.
Due to the complexity of the VRPSD, approximate methods have been mostly designed to solve
real size instances. Stewart and Golden [160] proposed a CCP and two SPR for the problem. The
SPR are dierentiated by the penalty induced by the recourse. In the rst one, a penalty is taken into
account disregarding the amount of the violation (lack of capacity). The second one induces a penalty
proportional to the amount of the violation. Using adaptations of the Clarke and Wright [39] and
the Generalized Lagrange Multipliers [159] heuristics, the authors solve some instances assuming the
demands are independent normal variables. Moreover, the authors present an approach to transform
the CCP formulation into an equivalent deterministic CVRP model. Also, solutions are provided
for the correlated-demands case. Dror and Trudeau [46] consider the models presented in [160] and
prove that in the VRPSD framework, the direction of the routes can have an important impact on
the objective function. This important fact shows the dierent structure that SVRPs take when
compared to deterministic VRPs. The Clarke and Wright [39] heuristic is adapted to incorporate the
expected recourse cost. More important, when two routes are being considered to merge, the direction
of the resulting route must be evaluated since this impacts the objective function. A comparison to
the adaptation of [160] shows that their method performs better in terms of the number of vehicles,
deterministic length and expected cost.
Bertsimas [22] considers the VRPSD as an SPR. The authors propose two strategies (recourse)
depending on the available information. Strategy a on the one hand, is the classical recourse policy
previously described. Strategy b on the other hand, assumes that demands are revealed before the
tours (routes) start, thus, customers with zero demand are omitted. Although, the last scenario seems
18
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
more suitable for a reoptimization paradigm, strategy b is interesting when resources do not allow
to perform a reoptimization scheme e.g when computational resources are scarce. Furthermore, this
study proposes closed-form expressions to calculate the expected costs of routes under general prob-
abilistic assumptions. Besides, under certain conditions (customer locations) the a priori strategies
perform very closely to the reoptimization paradigm. Further works of Gendreau et al. [66] led to the
development of a tabu search in the context of the VRPSD where additionally, customers might or
not be present. To calculate the expected cost, a proxy function is used to approximate the expected
solution costs.
Yang et al. [176] extended the classical recourse policy in their work: they assume that vehicles
return to depot when capacity is fullled but they can return before this event happens. When
leaving a customer, the capacity of the vehicle is compared to a threshold to determine if a visit to the
depot is valuable before attending the next customer. One threshold is associated with each customer
given a xed route. Moreover, this restocking policy is proven to be optimal given a xed route.
Two heuristics are proposed for the VRPSD with route durations limits for instances with discrete
triangular distributed demands. Bianchi et al. [23] consider the same restocking policy proposed
by Yang et al. [176] for the VRPSD. Their objective is to compare multiple metaheuristics, each of
them under two approaches for the local search: using the solution routes representation and a TSP
representation. The rst one evaluates the pertinence of local search movements given the cost changes
in the routes. The second one uses the deterministic tour length of an underlying TSP representation
of the solution to estimate if a movement is performed or not. Overall, the TSP approach can be seen
as an acceleration technique to improve the execution of the local search since recourse costs are not
calculated. Several metaheuristics are tested on instances with up to 200 customers.
Goodson et al. [76] approach the VRPSD using a cyclic-order representation of the solutions. The
classical recourse action is considered and demands are assumed to be Poisson distributed [38, 60]. An
ecient way to deal with neighborhoods under a solution cyclic-order representation is introduced.
This type of representation is based on a cyclic permutation of numbers, which is then decoded into
detailed routes. The authors use this type of representation since small changes in the permutation
can conduce to important changes in the decoded routes. Simulated Annealing is selected by the
authors to tackle the problem. The method achieves 16 out the 18 optimal solutions presented by
Christiansen and Lysgaard [38]. Later, Mendoza et al. [126] investigated the VRPSD with route
durations constraint. A hybrid metaheuristic composed of a GRASP plus Heuristic Concentration
(HC) are used. The solution method operates several simple heuristics to construct a pool of routes,
then, a set partitioning problem is solved giving a nal solution. The method solves the VRPSD with
classical recourse and achieves to nd all the best-known solutions of the Christiansen and Lysgaard
[38] testbed with average gaps of 0.02%. For the version with duration constraints two models are
considered, a CCP and an SPR.
Ak and Erera [3] proposed an alternative recourse policy called Paired locally coordinated (PLC).
The PLC extends the idea of usual recourse actions which consider vehicles separately to paired
vehicles. When the rst vehicle (type I) achieves its capacity it returns to the depot nishing its
service. Then, the second vehicle (type II) incorporates the unserved customers at the end of its
route. Besides, if the second vehicle faces a failure, it uses the classical recourse. The aim of this type
of coordination is to improve the expected total cost. A Tabu Search is used to solve instances with up
to 150 customers where customers have homogeneous discrete demands. Results show that comparing
to the classical recourse, the PLC allows signicant savings, ranging from 3% to 25% when instances
with 50 or more customers are considered. Erera et al. [50] worked on the VRPSD with hard time
windows. A limit on route duration is also considered, so drivers return to the depot respecting the
working hours. Although deliveries and analyzed data comprises a whole week, routes are created
for specic days. One of the main characteristics of the work is the fact that routes must if possible
visit the same customers. The motivation for this idea is to create long-term-relationships with the
19
2.2. VRP UNDER UNCERTAINTIES
customers along the horizon. Customers are classied into two groups, one that can be visited by
any driver and another group that must be visited at most by two drivers within a week. A CCP
formulation is used to constraint the probability of missing a customer' time window. The problem
is solved by means of a heuristic which uses Monte Carlo simulation to verify constraints satisfaction.
A novel recourse approach is used by creating primary and backup routes. The latter can receive
customers to guarantee feasibility or improve costs. A case study is presented and results are shown
for it.
Erera et al. [51] address the version of the VRPSD with duration constraints. Two recourses
are considered, the classical recourse and a variant where the vehicle performs a return to the depot
without servicing any demand if its current load is less than the demand met at the last customer,
then it comes back to this customer and satises its entire demand. To guarantee the route duration
constraint the authors propose to solve an Adversarial Problem. Since recourse actions generate
additional time depending on demands realization, the Adversarial Problem seeks to maximize the
additional time needed over all the possible demands values. Thus, solving this problem for a given
route allows to guarantee that in the worst case scenario, the duration constraint holds. A TS is
developed to solve the problem with independent demands which follow a discrete uniform distribution.
Results are presented for instances with up to 100 customers. Moreover, the inuence on the eet
size for the VRPSD with duration constraint is discussed. Although no general conclusions can be
drawn, the results indicate that duration constraints increase the required eet size. Mendoza et
al. [125] used a Memetic Algorithm (MA) to solve the VRPSD with multi-compartments. As far
as the calculus of failure probabilities is expensive, an approximation called Take-all-policy (TAP)
is employed. Furthermore, two heuristics are used to compare the eciency of the MA with TPA
to solve the problem. The rst one is based on an adaptation of the Clarke and Wright heuristic
proposed by Dror and Trudeau [46], the second one solves the deterministic problem with the MA
but reduces the capacity of the vehicles by a percentage. Demands are assumed to be independent
normally distributed and results are presented for instances with up to 484 customers. The MA mixed
with TAP approximation gives the best results in terms of number of vehicles and operational costs.
Other related variants of the VRPSD can also be found in the literature. A weight-related cost
version with dynamic recourse strategy is introduced in [122] while a multi-objective VRPSD is pro-
posed in [61]. Time dimension has been also considered, either in the form of time windows [114, 131]
or route duration constraints [176, 50, 51, 77, 126, 131]. Furthermore, an important version of the
VRPSD considering stochastic customers is discussed by Gendreau et al. [65, 66].
Regarding the reoptimization paradigm, the work of Dror et al. [45] introduced this approach
using a Markov Decision Process (MDP) to solve the single vehicle case. In this vein, single-vehicle
cases have been investigated in [151, 152, 153]. A multi-vehicle version was rst proposed by Goodson
[77] aiming to maximize the expected demand served.
Summing up, heuristics for the VRPSD have been mainly based on the adaptation of heuristics
initially designed for the CVRP [167, 160, 46, 125]. Similarly to the deterministic VRP, metaheuristics
have played an important role to solve the VRPSD. Tabu Search (TS) is the most used metaheuristic
in dierent variants of the VRPSD [66, 3, 51, 131]. Other methods such as the LNS [113], SA [76],
Memetic Algorithms (MA) [125], and hybridized metaheuristics e.g. Multi-Space Sampling [127, 126]
are addressed in the literature. Exact methods have been concentrated on L-shaped method [65, 83,
106, 25], branch-and-price [38] and branch-and-cut-and-price [60]. L-shaped methods have shown to
be able to solve instances with up to 100 customers but few number of vehicles [106]. Branch-and-
price and branch-and-cut-and-price on the other hand have been able to solve one instance with up to
100 customers [60]. Nevertheless, the former methods show a better performance when dealing with
multiple vehicles in the solutions [38, 60]. It shall be noticed that only the work of Biesinger et al.
[25] on the Generalized Vehicle Routing Problem with Stochastic Demands (GVRPSD) makes use of
exact methods considering a restocking policy, while the others remain on the classical recourse.
20
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
Moreover, it is evidenced that many of the works use their own set of instances [106, 176, 23, 3,
50, 51, 125, 126, 122] since no standard benchmark has been proposed. Meanwhile, other works have
rely on original or modied well-known data sets such as Nguyen et al. [131] and [114, 113] using
the Solomon [157] instances. Likewise, since the introduction of the Christiansen and Lysgaard [38]
benchmark other works [76, 60, 126, 122, 124] have used it as a base to compare their results (or
improve them).
VRP with stochastic customers
The VRP with stochastic customers (VRPSC) refers to the variant where the presence of the customers
is stochastic, i.e. they might be present or not. Among the classical stochastic parameters, this one is
the far less studied. The roots of the VRPSC are derived from the Traveling Salesman Problem with
Stochastic Customers (TSPSC). TSPSC was rst introduced in the thesis of Jaillet [85]. Both the
VRPSC and the TSPSC are modeled as a two-stage problem. In the rst stage, a solution to the TSP
is computed, while in the second stage, absent customers are revealed so the routes (tour) follow the
original order but skip the absent customers. Bertsimas and Howell [21] reviewed the main results of
Jaillet and proposed several heuristics based on classical neighborhoods ideas, e.g. 2-opt and 3-opt,
and others on angular sorting and space lling curves. A branch and cut approach is proposed in
[105] for the TSPSC. Bertsimas [22] also derives heuristics and bounds for the case when demand is
binary, i.e. either one or zero, and shows that the TSPSC is a special case of this problem. Further
development on the VRPSC is due to a more general problem, the VRPSC with stochastic demands
(VRPSCD). A tabu search is proposed in [66] while Gendreau et al. [65] use the L-shaped algorithm
to optimally solve the VRPSCD.
VRP with stochastic times
The Vehicle Routing Problems with Stochastic Times (VRPST) are versions of the VRP in which
the travel or/and service times are random variables. Table 2.3 presents a summary of some of the
most important works in the eld. Compared to the VRPSD, the VRPST is a more recent studied
version. The rst literature reference is presented by Laporte et al. [103] who considered the case with
stochastic travel and service times, with both CCP and SPR formulations. The authors use a two-stage
approach, designing an a priori solution before travel and service times are revealed. Moreover, in the
second stage the vehicles follow the a priori solution and a penalization for late arrivals proportional
to the length of the delay is considered. Optimal solutions are found for small instances with up to 20
customers and with at most ve travel time scenarios. An adaptation of the Clarke and Wright [39]
is used by Lambert et al. [100] for the version with stochastic travel times in a bank money collection
context. Similarly to Laporte et al. [103], a penalization is incurred on late arrivals, in this work, due
to the money lost of interest.
21
2.2. VRP UNDER UNCERTAINTIES
Table2.3:
Summaryof
VRPST
literature
Author(s)
StochasticParam
eters
Typeofmodel
Additionalconsiderations
Solutionapproach
Laporte
etal.[103]
TravelandServiceTimes
(Scenarios)
CCPandSP
RPenalized
late
arrivals
ClarkeandWright[39]heuristic
Lam
bertet
al.[100]
TravelTimes
(Scenarios)
SPR
Penalized
late
arrivals
ClarkeandWright[39]heuristic
WangandRegan
[174]
TravelandServiceTimes
CCP
Analyticalapproach
underhard
timewindows
KenyonandMorton[92]
Travel(uniform
)andService
Times
(Scenarios)
Com
pletiontimes
costand
probabiity
Durationconstraints
Branch-and-cutem
beddedin
Monte
CarloSimulation.
AndoandTaniguchi
[6]
TravelTimes
(Triangular)
SPR
Timewindowspenalization
GeneticAlgorithm
Lecluyseet
al.[111]
Traveltimes
(Lognorm
al)
Routeslength
meanand
variance
Timedependent
traveltimes
TabuSearch
Liet
al.[118]
TravelandServiceTimes
(Normal)
SPRandCCP
Timewindowspenalization
TabuSearch
Leiet
al.[112]
ServiceTimes
(Normal)
SPR
Timewindowspenalization
Generalized
Variable
Neighborhood
Search
Zhang
etal.[178]
Travel(Lognorm
al)andService
Times
(Normal)
Mixed
CCP&SP
RTimeWindows&Discrete
approximationon
ArrivalTimes
TabuSearch
Tas
etal.[163]
TravelTimes
(Gam
ma)
SPR
Timewindowspenalization
TabuSearch
Tas
etal.[164]
TravelTimes
(Gam
ma)
Mixed
CCP&SP
RTimewindowspenalization
Branch-and-Price
Ehm
keet
al.[48]
TravelTimes
(Normal)
CCP
Timewindows&Normal
assumptionon
initialservice
times
TabuSearch
Miranda
andConceição
[128]
TravelandServiceTimes
(Normal)
CCP
Timewindows&Discrete
approximationon
arivaltimes
Iterated
LocalSearch
Nguyenet
al.[131]
TravelTimes
(Ambiguous
distribution)
Hierarchicalobjectives
TimeWindows&Satiscing
Measure
Approach
TabuSearch
Erricoet
al.[53]
TravelTimes
(Discrete
Triangular)
Mixed
CCP&SP
RHardTimeWindows
Branch-and-Price
Binartet
al.[26]
TravelandServiceTimes
(DiscreteTriangular)
Mixed
CCP&SP
RHardTimeWindowson
mandatory
custom
ers
Two-phaseapproach.
22
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
More recently, Kenyon and Morton [92] tackled the version with stochastic service and travel
times under two dierent objectives. The rst one minimizes the maximum completion times of the
whole set of routes, while the second one maximizes the probability of completing the routes within
a pre-specied deadline. A Generalized Variable Neighbourhood Search (GVNS) for the capacitated
VRP with stochastic service times is designed in [112] using a penalization on route duration. Service
times are assumed to be normally distributed with a coecient of variation (CV) of 0.25. A closed
form to calculate the expected delay is presented under normality assumption. GVNS shows better
results at the expense of higher computational times in comparison to a VNS and a VND elaborated
by the same authors. An interesting study is conducted by Lecluyse et al. [111] for the VRPST
with stochastic time-dependent travel times. The authors consider four scenarios depending on two
relevant dimensions: speed proles and weather/road conditions. The proposed objective function
includes the expected duration of the routes but also its variability. This last is added through
the standard deviation, which is multiplied by a parameter β(β ≥ 0). Parameter β is intended to
represent the risk preferences of the decision maker. Results are obtained with a TS heuristic on 27
Augerat datasets. The authors conclude that their solution approach gives more robust and reliable
routes. In fact, their solutions compensate the increase in the expected route durations by diminishing
their variability, especially, when roads are congested for the most part of the day or when weather
conditions are bad.
When the VRPST is considered, one fundamental dimension to take into account is the time
windows. VRPST with soft time windows have been mainly studied [163, 164, 162] when compared to
hard time windows [53, 26]. However, many other works enforce the respect of the early time window
constraint but allow late services [6, 118, 178, 48, 128, 131], thus mixed soft and hard time windows are
used. One of the reasons for the use of soft time windows can be related to the convolution property.
The use of convolution property enables the deduction of closed form expressions to calculate the
recourse cost (SPRs) or the probability of constraints violations (CCPs). Hard time windows have
a truncation eect on the cumulative times, thus, convolution rules are no longer useful. Aiming to
overcome this problem some authors have used dierent techniques to model the arrival times when
hard time windows are considered: assuming that they can be modeled a well-known probability
distribution [36, 48], using discrete approximations [178, 128] to estimate their distribution or modeling
travel and service times by phase type distributions [75] and then approximate the distribution arrival
times.
Wang and Regan [174] present a theoretical development in the context of truckload transportation
industry. The authors consider the problem with stochastic travel and service times, where, travel
times incorporate the time for handling the loads. The authors show that in the context of hard
time windows, given a known distribution of the arrival time at the rst node, the same distribution
can be calculated iteratively for the next customers in the route. A CCP model is presented with
as objective maximizing the expected number of served customers, although no numerical results are
available. Ando and Taniguchi [6] studied the version with uncertainties in travel times within an
urban context in Osaka, Japan. An SPR is designed with penalization on early and late arrivals,
both costs are proportional to the time window violation. Although soft time windows are considered,
if a vehicle arrives earlier than the opening time window ei it must wait until it opens to perform
the service. Aiming to derive the distribution of the travel times, a study is conducted using probe
vehicles, recollecting time and speed information for several links. Nevertheless, since this information
is limited, they complement their inputs with a Vehicle Information Communication System (VICS)
which provides information on travel times, congestion, crashes, and car parks. A triangular distribu-
tion is proposed to model uncertainties about travel times, this is done for dierent zones of the city.
A case study is devised to compare the performance of usual routes against the ones created by a GA
which takes into account stochasticity. An improvement of around 4% is achieved for the total cost
(xed, operational and penalties costs). Moreover, the total running times were diminished by nearly
23
2.2. VRP UNDER UNCERTAINTIES
7%, and therefore, emissions of CO2, NOx and particle materials are reduced by around 7.5%
A similar problem is tackled in the article of Li et al. [118]. Both travel and service times are
assumed to be independent random variables following a normal distribution. Time windows are
considered in the same way as in [6], thus services are not allowed before the opening time window.
Two models are presented: a CCP and a SPR. The rst one bounds the probability of time windows
violation on both customers and the depot (route duration). The SPR, penalizes late arrivals regarding
time windows and route duration. To solve both formulations a modied TS is used. TS embeds a
Monte Carlo simulation to estimate probabilities of failure or the recourse cost. Results for own
generated instances with up to 100 customers are discussed. One of the main conclusions of the
authors is that CCP is overly constrained and harder to solve than the SPR. As a matter of fact,
when constraints on customers and route duration are simultaneously active, their method fails to
nd a feasible solution. A generalization of the models presented by Li et al. [118] is introduced by
Zhang et al. [178] proposing a combined SPR and CCP. The model allows to compare and make
trade-os between customers service and the operator costs by changing the associated constraints.
A computational study based on six Solomon [157] instances restricted to 20 customers is performed.
Travel times are assumed to be log-normal distributed while service times follow a normal distribution.
Coecients of variations range from 0.2 to 0.6 for the travel times and are xed to 0.4 for the service
times. The authors use a TS to solve the set of instances. Furthermore, a discrete approximation of
the arrival and service start time distributions, called α Discrete Approximation Method (αDAM), is
validated and used within the TS. αDAM considers a discrete set of possible values and the associated
probability mass function to represent the travel times, as well as the service times. A discrete
approximation of the arrival and service start times distributions is recursively derived. A comparison
with the normality assumption made by Chang et al. [36] shows the better performance of αDAM to
estimate the service levels, expected earliness and tardiness, and expected route costs. Nevertheless,
for the sake of fairness it must be mentioned that only one partial route involving 5 customers is
tested.
Ta³ et al. [163] deal with the pure soft time windows version of the problem under stochastic
travel times. In their work, services can start before or after the time window at the expense of a
penalization. The cost incurred by early or late services is dened as the service costs, while the
transportation costs account for vehicle travel costs and driver overtime. The two types of costs
are weighted in the objective function. Authors use the Gamma distribution to model the travel
times with various coecients of variation. To solve the problem a three-phase algorithm based on
TS is used. In the rst phase, the authors construct a solution by means of I1 heuristic proposed
by Solomon [157], without considering service costs. The initial solution is further improved by the
TS during the second phase. In the third phase, the best solution found by TS is improved by
changing the departure time of the vehicles. Since time windows are soft, the departure time can have
a signicant eect on total costs, by applying this post-optimization procedure the total costs are
reduced on average by 1.3%. Ta³ et al. [162] propose a column generation within a B&P procedure
to optimally solve the problem introduced in [163]. Using a set partitioning formulation, the authors
use a labeling algorithm to solve the associated Elementary Shortest Path Problem with Resource
Constraints (ESPPRC) [54]. Moreover, since time windows are soft, the only resource constraint is
the capacity of the vehicle. Due to this, the authors reduce the capacity of the vehicles for type 1
instances from the classical Solomon instances [157] to make them tractable. Results are provided for
20, 25, 50 some 100 customers instances. An extension of the work presented in [163] is developed
by Ta³ et al. [164] including stochastic time-dependent travel times (see also [111]). In this work,
the authors considered 5 intervals of time within a day in which travel speeds vary due to trac
conditions. Assuming that the time to travel a unit distance can be modeled by a Gamma random
variable, the mean and variance of the arrivals times are calculated in a closed form if customers
have no service times. If service times are considered, a rst order approximation is used (see also
24
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
[88]). Two approximate methods are proposed to solve the problem, a TS, and an Adaptive Large
Neighborhood Search (ALNS).
More recently, Ehmke et al. [48] studied the VRPST with stochastic travel times. The opening time
windows are considered hard while the time windows closure time windows are soft. No penalization is
considered for late services. The CCP formulation is solved by means of a TS. The approach used from
Ehmke et al. to estimate arrival and initial service times is quite similar to the one used by Chang et al.
[36]. Thus, arrival times are assumed to be normally distributed and initial service times are modeled
by truncated (at ei) normal random variables. A statistical study shows that even if travel times follow
shifted exponential or shifted Gamma distributions, assuming normality for the arrival times allows a
reliable estimate of metrics such as the mean, standard deviation and the 95th percentile for the start-
service times. This approach has the advantage of being easily incorporated into classical methods
for the VRPTW. Stochastic travel and service times are considered by Miranda and Conceição [128].
A CCP formulation is used so late arrivals are constrained by a desired service level. Early services
are not allowed but late services are permitted. The truncated random variables are modeled using a
discrete approximation of the arrival and service start times distributions similar to the one of Zhang
et al. [178]. An exhaustive comparison to αDAM is presented showing an improvement in various
metrics. Moreover, time consumption is greatly improved. The discrete approximation is embedded
within an ILS algorithm to solve some Solomon [157] instances.
Nguyen et al. [131] present an interesting approach for the VRPST with stochastic travel times.
A Satiscing Measure Approach (SMA) is derived to evaluate the dissatisfaction of the customers for
late arrivals. Late services are allowed and early arrivals are forbidden. Travel times are assumed to
follow an ambiguous distribution, though, for simulation purposes it is assumed that they follow a
Gamma distribution. The SMA is incorporated into the objective function as main objective (among
a hierarchy). By construction of the SMA, this implies a minimization of the expected tardiness at
customers. Their approach is embedded within a TS algorithm showing a remarkable performance;
moreover, the results show that by incrementing the number of vehicles used, as well as the distance
of the routes, the expected tardiness at customers can be signicantly reduced. Errico et al. [53]
extended their work in [52] dealing with stochastic travel times and hard time windows. In their more
recent work, the authors used a combined SPR and CCP to solve the problem. Penalties are incurred
each time a customer is not provided with a service. Furthermore, customers that are not served
are picked up based on the two proposed recourse actions. Moreover, customers may be avoided if
they induce the route to be operationally infeasible. In such a case, the current or the next customer
is selected to be omitted. Also, two constraints are added to ensure that the probability of having
no failure at a route reaches a required threshold and limit the number of recourses to at most one.
Another interesting point in this work is the way in which information is revealed. When a vehicle
arrives at a customer, it spends some xed time to determine the actual service time. Only then
the choice to avoid current or next customer is made. Service times are assumed to be discrete
random variables with triangular distribution and two cases regarding the range of possible values are
considered for experiments. The problem is modeled as a set partitioning problem and solved through
a branch-and-price.
Binart et al. [26] also studied the problem with hard time windows. In fact, their model considers
two types of customers, mandatory and optional. Only mandatory customers have an associated
time window. Uncertainty aects both the service and travel times which are modeled by discrete
triangular distributed random variables. Moreover, multiple depots are considered. A two-phase
approach is used to solve the problem. During the rst phase a skeleton of routes serving mandatory
customers is created. During the second phase, optional customers are added to the planned routes
which are modied in real time to enable time windows to be respected. Furthermore, in the second
phase, the routes are evaluated in an SPR and CCP context. Penalties are included if the time window
is missed, while the probability of success is also constrained.
25
2.2. VRP UNDER UNCERTAINTIES
In summary, VRPST have been solved mostly with the use of metaheuristic methods because of
the complexity of the problem. The solutions approach usually rely on a static approach. Simple
heuristic methods have been rarely used [100]. Within metaheuristics, approaches such as TS [148,
118, 48, 163, 178, 164, 131] are the foremost used, other metaheuristics such as ALNS [164], GNVS
[112], GA [6], and approximate methods with Branch-and-cut based frameworks [92, 1] have also been
used. Exact methods are limited to branch-and-cut [103], branch-and-price [162] and branch-and-cut-
and-price [53]. The use of these approaches is limited to cases where few scenarios are considered
[103], when convolution properties enable closed forms evaluations [162] or when discrete variables are
considered [53].
2.2.2 Robust Optimization
While the SVRPs have been mostly solved using the Stochastic Programming paradigm other ap-
proaches in the literature can be found. In fact, stochastic models have been criticized on three main
points: the underlying probability distributions must be known in advance, convolutions must be com-
putationally tractable and provided solutions might be infeasible for some realizations of the random
variables [156]. Actually, if the underlying probability distributions are unknown or information is
very limited this precludes the use of SVRPs. Robust Optimization (RO) oers a dierent approach to
deal with uncertainties. In fact, uncertainties become deterministic and set-based [20]. The objective
is to create solutions that are feasible for any realization of the uncertain parameters rather than being
immunized in a probabilistic sense [20]. The interested reader is referred to the book of Ben-Tal et
al. [16] and the paper of Bertsimas et al. [20] as excellent introductory points.
Although less studied than SVRPs, robust optimization has also been applied to VRP problems.
Sungur et al. [161] tackles the Robust Vehicle Routing Problem (RVRP) with demand uncertainty.
Demands are uncertain but belong to a bounded set. This bounded set is intended to represent
deviations around the expected demand values. The authors consider three sets, namely, convex hull,
box and ellipsoidal. However, most of the results are given for the convex hull set. Furthermore, it
is shown that under these bounded sets, the RVRP with uncertain demands can be formulated as a
Mixed Integer Linear Problem (MILP), using a BC method the problem is thus optimally solved. An
interesting comparison for dierent deviations in the demand uncertainty sets are presented. Moreover,
in this study CCP and SPR models are also compared to the RVRP. CCP oers similar results to the
RVRP when deviations are small, still when this parameter is increased CCP becomes infeasible. In
general terms, SPR oers solutions of equal quality to those provided by the RVRP or even better under
high deviations. The same problem is considered by Gounaris et al. [79]. Demands are assumed to be
supported on budget and factor model sets. Furthermore, the authors state the conditions to reduce
the RVRP with uncertainties in demands into a deterministic VRP. Several classical formulations for
the CVRP are examined as well as a new assignment formulation. Results present the performance
of the dierent formulations giving the use or not of robust rounded capacity inequalities. Gounaris
et al. [78] extended the previous work developing an Adaptive Memory Programming Framework
metaheuristic. This method allowed them to solve instances with up to 483 customers. A conclusion
of the work is that robust solution, which increment on average 5% the cost, are able to handle the
uncertainty even with a considerable augmentation in demands.
Solano et al. [156] tackle the robust VRP with uncertainties in the arc costs (equal to time).
The authors consider the variability in travel times using multiple scenarios. Each scenario takes into
account the cost for the whole set of arcs in the problem. An exact MILP is used to solve small size
instances. For bigger instances containing up to 100 customers and as much as 20 scenarios, several
heuristic and metaheuristics are devised. Among the solution approaches, the Multi Start ILS gives
the best results in terms of quality, averaging a gap of only 0.35%.
Robust Optimization is also used to tackle VRPs with times uncertainty. Agra et al. [2] deal
with the VRP with time windows and uncertain travel times inspired by maritime transportation.
26
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
Travel times are assumed to be determined by an uncertain polytope. Two formulations are proposed
based on resource and path inequalities, and are solved by means of either a branch-and-cut or a
column-row-generation algorithm. Several results are presented for instances with up to 50 nodes.
Moreover, dominance rules are presented for the path inequalities version showing that the number of
extreme points to represent the polytope can be largely reduced. Chen et al. [37] work on the version
with uncertain service times in an Arc Routing Problem context. A BCP is used to solve the problem
considering a restricted budget of uncertainty on routes. The maximum deviation of the service times
can be the double of the nominal time. Three dierent deviations are studied (0.4, 1.2, 2.0). The
results are compared to a CCP with constraints on route durations. For the considered problem, the
RO gives less costly routes which by the way, are commonly feasible in the CCP. Adulyasak and Jaillet
[1] propose to solve the RVRP with deadlines and travel times uncertainty. Moreover, the authors also
tackle the SVRP variant under the same type of uncertainties. For the RVRP a performance measure
called lateness index is dened to minimize the risk of violating deadlines, while the SVRP formulation
uses a sampling approach similar to the one in [92]. Furthermore, an extension to consider soft time
windows is described, in the case of the RVRP this is achieved by adding an earliness index. To
solve both formulations, the author use a branch-and-cut approach. Results are reported for instances
considering directed graphs with up to 80 customers and 240 arcs. A comparison between the SVRP
and the RVRP shows that SVRP heavily resides in the prior knowledge of the underlying probabilistic
distributions, while the RVRP outperforms the SVRP if this information is incorrect.
2.2.3 Fuzzy Logic
Fuzzy logic has also been applied to deal with VRP under uncertainties. In fact, given the inherent
variability of the problems parameters, fuzzy numbers and variables can be used to model these
parameters using the fuzzy logic sets. Fuzzy sets work dierently than deterministic sets. In the
former, a number has a degree of pertaining to the set with a grade of membership which ranges
between zero and one [4]. Deterministic sets are thus a particular case of the fuzzy ones where partial
membership to a set is not allowed, therefore, the grade of membership is fully dened by either one
or zero. Moreover, fuzzy numbers can be dened as convex and normalized fuzzy sets. Using fuzzy
numbers, the parameters of the problem can be represented given that the underlying probability
distribution is unknown or hard to be computed. For further details on Fuzzy variables the interested
reader is referred to [91, 155].
Teodorovi¢ and Pavkovi¢ [165] consider the VRP under demands uncertainties. Using the fuzzy
set theory, the authors model the demand at each customer as a triangular fuzzy number. Moreover,
by using fuzzy arithmetic, it is shown that the remaining capacity after visiting a customer can also be
represented by a triangular fuzzy number. Two approximate reasoning algorithms are then proposed.
The rst one, considers only the remaining capacity to decide if another customer should be serviced
by a partial route. The second one, considers both the remaining capacity and the customer demand
to decide if it should be included in a route or not. To solve the problem, the sweeping algorithm
[70] is modied to consider the fuzzy logic. The solution is further evaluated within a simulation
procedure to calculate the expected distance traveled due to failures. The classical recourse action
is considered. Results are reported for a single instance with 100 customers with an improvement
when the second reasoning algorithm is selected. A similar problem is considered by Cao and Lai
[35] addressing the Open VRP with fuzzy demands (OVRPFD). The problem is modeled as a CCP,
making use of fuzzy credibility measure to guarantee customers satisfaction. Demands are assumed to
behave as a triangular fuzzy number and the decision maker can select a preference index representing
is risk attitude towards the possible failures. Moreover, the classical recourse for the VRPSD is used
when failures arise. The OVRPFD is solved with a hybrid method combining Monte Carlo simulation
and a dierential evolution algorithm. Results are presented for instances with up to 100 customers.
Kuo et al. [96] worked on the VRP with time windows in a context where both travel times and
27
2.3. CONCLUSION
time windows are modeled through fuzzy numbers. Both parameters make use of the triangular
membership function. To solve the problem, a two phase algorithm is proposed. In the rst part, an
Ant Colony optimization procedure is used to solve the associated TSP. Then in a second phase, routes
are constructed using as input the results of the AC procedure. Routes are considered feasible if they
respect the capacity and if the time windows respect a required service level. This one is considered
as the largest membership value found in the intersection of the fuzzy arrival time and time window.
Some results are presented for a case study with 50 customers and dierent types of time windows.
He and Xu [81] deal with the VRP with uncertainties in customers demands and travel times. In this
study, the authors consider travel times as independent normal random variables, and the demands
as fuzzy variables. In fact, demands follow a normal distribution with a mean value represented by
a trapezoidal fuzzy number. The variance of the demand is assumed to be crispy. A CCP model
is deducted to guarantee a service level on the vehicles capacity and customers time windows. The
CCP model is solved by means of a GA and limited results are shown for an instance with more than
200 customers. Zheng and Liu [179] propose a VRPTW with uncertainties in the travel times. A
CCP is used to model the problem, considering a service level on customers time windows. Using
Monte Carlo simulation embedded within a GA, the authors solve a problem with 18 customers given
triangular fuzzy variables. Xu et al. [175] consider the VRPTW with soft time windows. Uncertainty
is considered for the tolerable starting time, which is modeled through a random fuzzy variable. That
is, services can start earlier than ei or later than li, but within endurable earliness and lateness
times. Therefore, uncertainty aects the time windows. The proposed model aims to minimize the
operational costs and maximize the average satisfaction level of customers. This satisfaction level
depends on the service starting time, whether it is outside the time window, during endurable times
or within the time window. A particle Swarm Optimization (PSO) is used to solve the problem and
results are presented for a case study application with 18 customers.
2.3 Conclusion
The VRP is an active research eld. The last sixty years have shown a massive amount of research
within the overwhelming dierent types of VRPs. Although most of the research has been concentrated
on deterministic problems, in the past three decades an important amount of time was devoted
to variants that consider the inherently uncertainty of information in many applications. How the
uncertainties are handled is hardly dependent on the information available, e.g. if the underlying
probability distribution of the parameters is known or not.
Stochastic optimization has been the main approach to solve uncertain VRPs. Even though SVRP
eld is still at an early stage of development [63] it has an active community. Still some issues can
be detected in the reviewed bibliography. For instance, many of the works deal with small to at most
medium instances. That is the case for the VRPSD and the Cristiansen a Lysgaard [38] benchmark
whose instances contain at most 60 customers. Incrementing the instances size of the testbeds can push
knowledge boundaries by challenging the creation or adaptation of new methods (exact and heuristic)
to solve these problems. Moreover, this implies to design strategies that exploit characteristics of the
SVRPs, while overcoming the inherent computational challenge of solving the SVRPs.
In SVRPs with stochastic times problems involving soft time windows (or mixed soft and hard
time windows) are more common while the hard time windows case is very rare. Additionally, when
hard time windows are considered, usually discrete probability distributions are used to handle the
problems complexity. Thus, SVRP with hard time windows remains a fairly unexplored area in the
eld. Besides, the largest amount of articles in the literature rely on convolution properties (also
for the VRPSD) to deal with stochastic parameters. Even if convolution can arise naturally when
dealing with random parameters, there is no proof that it is always (or recurrent) the case for SVRPs.
Therefore, solution approaches should include mechanism to be exible when convolution properties
28
CHAPTER 2. LITERATURE REVIEW - VEHICLE ROUTING PROBLEMS
do not hold. This without forgetting the tradeo between accuracy and exibility.
Given the before, this rst part of the thesis focuses on solving the SVRPs for large instances as
a way to create new comparable results. This is achieved by adapting solution methods with tailored
strategies for the SVRPs, particularly for the VRPSD in chapter 3. Then a SVRP with stochastic
travel and service times and hard time windows is considered in chapter 4.
29
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40
Chapter 3
Hybrid metaheuristic for the VRPSD
3.1 Introduction
Since its introduction by [9], the Vehicle Routing Problem (VRP) has been widely studied in the
literature, becoming a classical combinatorial problem. While the VRP comprise a broad family of
problems, it is commonly used to refer to the Capacitated Vehicle Routing Problem (CVRP) version.
The CVRP objective is to build the set of vehicle routes with minimum cost satisfying customers
demands. Stochastic Vehicle Routing Problems (SVRPs) are generalizations of the VRP where one
or more parameters of the problem are associated with random variables. Recently, more attention
has been paid to SVRPs. Their importance relies on their closeness to reality and their ability to take
into account variability of data. A recent review of the main SVRPs variants studied in the literature
can be found in [18].
To model SVRPs two stochastic approaches have been widely used in the literature: Stochastic
Programming with Recourse (SPR) and Chance Constraint Programming, using probabilistic con-
straints (CCP). Contrarily to CCP, the rst approach considers actions (called recourse) to overcome
or react to possible violations of the constraints. In fact, since parameters are random variables some-
times constraints might not hold leading to failures. SPR takes into account the cost associated with
recourse within the objective function. The CCP introduces constraints to limit the probability of
failures to a threshold aiming to guarantee a quality level of the solution. It should be noted that
SPRs and CCPs are not exclusive and can be used in mixed formulations (see for example [12]).
The Vehicle routing problem with stochastic demands (VRPSD) is an extension of the VRP in
which the demand of each customer is a random variable. VRPSD was originally proposed by [40] who
solve the problem through a modication of the well-known Clarke and Wright heuristic ([8]). Exact
methods can be found in the VRPSD literature and are divided in two approaches: the L-shaped
algorithm (see [23]) and branch-and-price based. L-shaped methods ([?], [21], [24], [22], [4], [35]) have
been the preferred approach to solve the VRPSD and are able to optimally solve instances with up
to 100 customers and few vehicles when discrete distributions are considered ([24]). In [22] normal
distributions are used to model the demands attaining optimal solutions to instances with 60 to 80
nodes and two to four vehicles.
Branch-and-price based methods have shown to solve problems with larger number of vehicles. The
rst branch-and-a-price algorithm for the VRPSD is presented in [7] to deal with instances with at
most 60 customers under a Poisson demands assumption. More recently, a branch-and-cut-and-price
algorithm is implemented by [15] and tested on instances with up to 101 customers and 15 vehicles.
Heuristics and metaheuristics methods are more often used to solve the VRPSD. In [39] a CCP and
two SPR models are provided for the problem at hand, which is then solved by means of the Clarke
and Wright heuristic [8] and a Lagrangian Relaxation based heuristic for instances with normally
independent and correlated demands. In this study, a transformation of the VRPSD into CVRP
41
3.1. INTRODUCTION
under certain conditions is also discussed. [?] propose a Tabu Search called tabustoch to tackle the
extension of VRPSD where additionally, customers are present or not with a given probability.
[41] consider the VRPSD under restocking possibilities, thus adopting strategies for preventive
restocking. That is, return trips to the depot to restock even if the vehicle is not empty to avoid future
failures. The authors develop and embed an optimal restocking policy in the route design. In [3],
hybridization of ve metaheuristics with dierent objective function approximations and preventing
restocking is devised. More recently, [20] propose a Simulated Annealing procedure to solve the
VRPSD that uses a cyclic-order encoding to represent solutions. The authors designed a two-phase
technique embedded in the solution method. In the rst phase they consider only deterministic costs,
while the recourse cost is explicitly integrated into the second phase.
[28] use a Memetic Algorithm to solve the multi compartment VRPSD which is a generalization
of the classic VRPSD. In the multi compartment VRPSD, the customers are associated to several
products demands that cannot be mixed. This constraint imposes to load each product in a dierent
compartment. In a more recent study, [29] designed a Greedy Randomized Adaptive Search Procedure
(GRASP) enhanced with Heuristic Concentration (HC) to solve the VRPSD with maximum route
duration constraints. To the best of our knowledge, the method of [29] reports the best overall results
for [7] testbed.
Other variants of the VRPSD have also been considered in the literature. [27] addressed the
VRPSD with weight-related costs and solved it by means of an Adaptive Large Neighborhood heuristic
using several approximate methods. [37] dealt with the VRPSD with dynamic requests meaning that
previously unknown customers can be received and scheduled over time. A Variable Neighborhood
Search (VNS) based approach was proposed by the authors to solve both stochastic and dynamic
cases.
[32] study the VRPSD with time windows using a Satiscing Measure Approach (SMA). The SMA
is embedded in a tabu search showing very competitive results in small computational time. A multi-
objective version of the VRPSD considering total traveling distance, total driver remuneration, number
of vehicles and drivers remuneration balance is proposed in [16] and solved using a multi-objective
evolutionary algorithm.
Concerning the stochastic models, SPR formulations have been dominant in the VRPSD literature
compared with CCP formulations ([38, 39, 10]). The most used recourse policy, called from now
onward in this paper the classical recourse, is dened as follows. When the charge of the vehicle is
emptied (fullled) it returns to the depot to replenish (unload) the charge, and resumes its assigned
route from the failure point ([2]). However, other recourse policies have been studied and implemented
through several studies: preventive restocking policies are extensions of the classical recourse where
return trips to the depot are performed even if the vehicle is not empty to avoid future failures
([41, 3, 4, 27, 42, 35, 36]); pairing strategies allow the cooperation of multiple vehicles ([1]); split
deliveries between paired routes ([26]) in which some customers are served by two vehicles; and
backup routes ([11]) that receive customers from primary routes.
Although the use of more complex recourse policies can represent a signicant saving relative to
simpler ones ([1]), the latter have been preferred since they allow more tractable models and stable
tactical routes ([17]). For this reason only the works of [4] and [35] deal with exact methods for the
VRPSD using recourse actions dierent than the classical one. [4] consider a restocking policy for the
generalized VRPSD using a single vehicle. In [35] optimal restocking policies allow vehicles to decide
between a visit to the depot to replenish or proceeding to the next customer. Decisions are made
using an optimal remaining capacity threshold for each customer within the route. The authors show
that under arbitrary discrete probability distributions, instances with up to 60 customers and four
vehicles can be solved. Moreover, it is shown that restocking policies can reduce the recourse cost to
half of those achieved by the classical recourse.
This chapter is dedicated to the vehicle routing problem with stochastic demands using the clas-
42
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
sical recourse. We propose a Memetic Algorithm hybridized with a GRASP (MA+GRASP) to solve
eciently the VRPSD. The GRASP framework is embedded within a MA and is used as a way of
restarting the algorithm. In this chapter, it is shown that the MA+GRASP is a valid and ecient
method to solve the VRPSD. Moreover, a new testbed built from instances originally designed for the
CVRP with up to 385 customers is proposed. The obtained results on new large instances serve as
benchmark for testing future methods in real life scale problems. The remainder of this chapter is
organized as follows. The problem formulation is introduced in section 3.2. Section 3.3 presents the
developed solution approach for the problem. Numerical results are given and discussed in section
3.4. Finally, section 3.5 concludes the chapter.
3.2 Problem formulation
The traditional CVRP can be described as follows. Let G = (V,E) be a complete undirected graph
where V = 0, 1, . . . , i, . . . , n and E = [i, j]∀i, j ∈ V | i < j are the vertex and the edge sets
respectively. Moreover, V c = V \ 0 is the customers subset, each customer has a non-negative
demand qi ∀i ∈ V c. Vertex 0 stands for a central depot where a homogeneous set of vehicles with
a limited capacity Q each are initially located. Furthermore, each edge [i, j] ∈ E has a non-negative
cost cij . The objective is to build a set of routes with minimum cost considering that each route must
start and end at the depot, the maximal capacity Q must be respected and, no split deliveries are
allowed. This last constraint means that each customer is serviced once by a vehicle which deliver (or
pickup) its whole demand.
The problem tackled in here presumes that the demand qi of each customer i follows a probability
distribution ψ, with expected value and variance noted E [qi] > 0 and V ar [qi] > 0 respectively. We
assume that probability function ψ is known and demands are mutually independent. Furthermore, we
consider as other authors ([7, 15, 20, 29]), that ψ distribution has a cumulative property, i.e. the sum
of demands probability functions is also ψ distributed. Furthermore, the vehicles eet is assumed to be
unlimited and no xed cost per vehicle is involved. This problem is formulated in this work as an SPR
in which the rst recourse policy of Bertsimas [2] is employed. This recourse (classical) assumes that
whenever a route achieves its maximum capacity Q, the vehicle returns to the depot to load/unload,
then it returns to the customer where the capacity was fullled to complete the unserviced demand
and then continues its route from the failure point. Let r be a route dened as a sequence of nodes
r = r0 = 0, r1, . . . , ri, . . . , rk, rk+1 = 0, the cumulative demand up to a client ri in a route r can be
dened as Dri =∑ij=1 qrj with E [Dri ] =
∑ij=1E [qrl ], V ar [Dri ] =
∑ij=1 V ar [qrl ]. The expected
recourse for a given customer ri ∈ V c in a route r is estimated by equation 4.1, as done by [15].
ERCri = 2 · c0ri ·
[ ∞∑u=1
P(Dri−1
≤ uQ)− P (Dri ≤ uQ)
](3.1)
Given a route r, the term P(Dri−1
≤ uQ)stands for the probability of cumulative demand up to
customer ri being less than or equal to a multiple of the capacity of the vehicle. Therefore, the
probability part of the expression represents the sum of having the uth failure of the route at client ri.
It shall be noticed that this expression does not consider the case when the remaining capacity equals
the demand of a customer (exact stock-out). In such case the vehicle can return to the depot and
then, continue towards the next customer in the route. Indeed, Hjorring and Holt [21] have already
addressed the exact stock-out recourse. Nevertheless, we keep expression (4.1) since probability of such
events might be rather low and its consideration can make the calculation computationally inecient.
Hence, the expected cost of a given route r can be calculated using equation 4.2.
E [Cr] =
k∑j=0
crjrj+1 +
k∑j=1
ERCrj (3.2)
43
3.3. SOLUTION APPROACH: HYBRID METAHEURISTIC
In order to avoid multiple failures in a route r, the expected demand is limited to be at most equal to
the maximum capacity Q, this assumption has been already used by many authors such as Laporte
et al. [24], Christiansen and Lysgaard [7], and Gauvin et al. [15]. Indeed, if constraint (4.3) is not
included, the optimal solution tends to be composed by only one route which visit all the customers.
k∑i=1
E [qri ] ≤ Q ∀r (3.3)
The objective is thus to create a set of routes with minimum expected cost calculated by means of
equation 4.2 and respecting constraint 4.3.
3.3 Solution approach: hybrid metaheuristic
A hybrid metaheuristic, combining a Memetic Algorithm (MA) and a GRASP, is proposed to solve
large VRPSD instances. The proposed method is described in Algorithm 1. The MA basis is borrowed
from the ideas of Prins [33] and it works with a xed population size. MA starts by creating an ordered
initial population called Pop (line 1 Algorithm 1). New individuals are created from the crossover
of two chromosomes selected from Pop (line 5 Algorithm 1). Moreover, a mutation procedure can
be performed on the new individual with probability pmp (line 6 Algorithm 1). A local search is
then executed with an associated probability of pls (line 8 Algorithm 1) on the resulting solution. A
procedure called Split is used to evaluate an individual tness (lines 7 and 10 Algorithm 1) and is
presented in section 3.3.1. It allows also to convert chromosomes to VRPSD solutions by computing
the detailed routes. Furthermore, when the local search is carried out, the routes are concatenated
before using Split (line 10 Algorithm 1). To ensure a diversity on the population, clones are not
allowed (line 12 Algorithm 1). Indeed, a new chromosome is kept for the next iteration only when
its distance to the current population is not null. The distance measure used is the broken pairs [6]
that counts the number of times a pair of consecutive customers in a rst individual is broken in a
second one. When the new individual is accepted to enter the population, it is added to this last in a
position that keeps the population ordered (line 13 Algorithm 1).
Since the MA works with a xed size population, when a new chromosome is entering the popula-
tion, another one already in Pop is removed. This last is randomly selected among those with tness
superior to the median (line 13 Algorithm 1). Moreover, the MA uses a restart procedure. Indeed,
after each φ iterations without improving the best solution the MA discards all the individuals ex-
cept the best one (line 17 Algorithm 1). The population is completed using a GRASP procedure as
explained in section 3.3.2. The algorithm stops when a time limit τ is achieved or if ρ iterations have
been performed without improving the best solution found so far (line 4 Algorithm 1).
44
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
Algorithm 1 MA + GRASP1: Pop← Initialize population2: φ← Constant > 03: i← 14: while not (stop) do5: c← crossover (Pop)6: Mutation, pmp (c)7: Split (c)8: Local Search, pls (c)9: if Executed Local Search then10: Concatenate and Split (c)11: end if12: if Is Not Clone (c) then13: Update Population (c,Pop)14: end if15: Update(i)16: if i ≥ φ then17: Restart with GRASP (Pop)18: i← 119: end if20: end while
3.3.1 Chromosomes
The MA + GRASP uses a twofold representation for each individual. The rst one follows the
idea of Prins [33] and consists in representing a solution by a permutation of the V c customers.
This representation have been already used by authors such as Mendoza et al. [28] for the multi-
compartment VRP with stochastic demands, Mendoza et al. [30] and Goodson et al. [20] for the
VRPSD, and Mendoza et al. [29] for the VRPSD with time time duration constraints. The second
one gives the detailed routes composing the solution. In order to decode the permutation of customers
into a set of routes, the Split procedure presented in [33] is employed.
Split works by constructing a directed graph H = (W,Y ) composed by its vertex set W =
W 0 = 0,W 1, · · · ,W i, · · · ,Wn, where W 0 serves as a dummy auxiliary vertex while the rest of
vertex W 1, · · · ,W i, · · · ,Wn ∀i ∈ V c are the permutation of the V c customers. The arcs set Y is
built in way that every arc (W i,W j) | j > i represents a feasible route starting at the depot, visiting
customers W i+1, · · · ,W j and returning to the depot. Since each arc is associated with a feasible
route r, the arcs have an associated weight equal to E [Cr]. Therefore, it is during the construction
of routes (arcs) composing graph H that stochasticity is considered, by properly calculating the costs
using equation (4.2). After constructing the set of all feasible routes (respecting constraint 4.3) the
goal is to nd the shortest path from vertex W 0 to Wn and thus the arcs composing the shortest path
are the optimal partition for the permutation of customers in the ordered sequence to routes. The set
of routes is, in fact, the second representation of the individual, and is used when the local search is
performed. For more details about Split method the reader is referred to [33].
Figure 3.1 presents an example of the Split procedure on a VRPSD instance composed by ve
customers (V c = 1, 2, 3, 4, 5) and where vehicles have a limited capacity Q = 50. Moreover, it is
assumed that all clients have a stochastic demand which follows a Poisson distribution with mean
20 units, that is qi ∼ Poisson (20)∀i ∈ V c and costs (cij) are equal to the distances. Part (a) of
gure 3.1 represents a permutation of clients that is going to be decoded. Part (b) gives the necessary
distances information to decode the permutation. Part (c) stands for the Split auxiliary graph, note
that each arc has an associated cost (calculated by means of equation (4.2)), furthermore, the dashed
arcs are the ones used in the solution of the shortest path. Take for example arc (2, 1), it represents
the route r = 0, 4, 1, 0 and has an expected cost of E [Cr] = 88.68, the cost can be further divided
into deterministic cost 85 units, and expected recourse cost 3.68 = 88.68 − 85. Figure 3.1 part (d)
shows the optimal decoding of the permutation into routes, with the cost of the whole set of routes
which is the tness or cost of the individual.
45
3.3. SOLUTION APPROACH: HYBRID METAHEURISTIC
0
2
4
1
5
3
2 4 1 5 30 1 52 4 3
15
10 10
5
20
40
35
20
10
Clients permutation (a)
40 80 70 40 20
79.21 67.11
88.68 36.05
Split auxiliary graph (c)
Decoded routes from permutation (d)Individual cost: 40.00 + 88.68 + 36.05 = 164.74
Distance information (b)
0
2
4
1
5
3
Figure 3.1: Split example for the VRPSD.
0
1
2 34
5
0
1
23
4
5
Solution 1 Solution 2
Figure 3.2: Broken pairs distance example.
Concatenation procedure allows to pass from a set of routes representation to a permutation one.
It is achieved by removing the depot node from the start and end of the routes. Then, the customers
sequences of the routes are added one after another. See for example the part (d) of gure 3.1, where
routes 0− 2− 0, 0− 4− 1− 0, and 0− 5− 3− 0 derive to the customers permutation 2− 4− 1− 5− 3.
Besides, the calculus of the broken pair distance [6] is performed using the routes representation.
Figure 3.2 shows two solutions for which the number of broken pairs is to be calculated. Solution 1
serves as base for the comparison while solution 2 is a candidate to enter into the population. The
distance measure is two since the arcs 2− 3 and 4− 5 present in solution 2 are not present in solution
1.
3.3.2 Initial population and Restart
Population Pop is initially lled with three individuals created with the well-known heuristics: Clarke
and Wright [8], Gillet and Miller [19] and best insertion. Each of the aforementioned heuristics is
executed twice with slightly dierences in the capacity Q. In the rst call to the heuristics, the full
capacity of the vehicles (Q) is considered, while in the second one the capacity of vehicles is reduced
to Q′ = 0.9 ·Q. That is, constraint (4.3) right side is changed to 0.9 ·Q. By doing so, it is expected
that constructed routes will have a lower probability of failures (see [28]), introducing important
information to the population. These heuristics are modied to consider the associated recourse costs.
The routes obtained by each heuristic are then concatenated to obtain a chromosome which is then
evaluated by the splitting procedure described in 3.3.1. Only the best three individuals among the
46
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
six generated ones are kept in the initial population, the other three are discarded. To complete the
population Pop, the remaining chromosomes are created from completely random permutations of
customers which are next evaluated with the Split procedure. The population size is denoted Popsize
afterward in the paper.
Restart
Restart procedure is executed after performing φ iterations without improving the best solution found
so far. Algorithm 2 shows the pseudo-code of the procedure. It starts by deleting all the individuals
of Pop except the best one. In order to generate Popsize − 1 missing individuals the next strategy
based on a GRASP procedure is used. The GRASP generates⌈(Popsize−1)
2
⌉individuals. Meanwhile,
the remaining ones required to achieve Popsize are created from completely random permutations
of customers, which are next evaluated with the Split procedure. This approach is used to ensure
diversication within the restart.
Greedy Randomized Adaptive Procedure
Greedy Randomized Adaptive Search Procedure (GRASP) is a metaheuristic introduced by Feo and
Rasende [13]. GRASP consists in using a randomized constructive procedure to build solutions that
are improved after by a local search approach. This process is repeated through a number of iteration.
The behavior of the constructive procedure is controlled thanks to a parameter called greediness which
permit to balance between greediness and randomness.
The proposed GRASP works as follows. Iteratively, the Greedy procedure of the GRASP, which is
based on the Nearest Neighbor (RNN) heuristic is called. The RNN works by creating a permutation
of the customers. At each iteration, the RNN picks randomly a client among the k nearest neighbors
to the last visited customer (initially it starts from the depot), and add it to the permutation. After
several preliminary tests we picked k with the expression Max(2,⌈|V |60
⌉) and iterations to a value of
10. The RNN works without considering recourse costs as in [3, 20]. After generating a permutation
by using the RNN, it is decoded using the Split procedure. The resulting solution undergoes just
after the local search procedure (see section 3.3.4) considering only deterministic costs to avoid the
overhead of considering recourse costs. The best solution found by this steps is selected and kept.
Then, the local search procedure considering the recourse cost is called to improve the individual.
This last is added to the population and the process is repeated until⌈(Popsize−1)
2
⌉individuals are
added.
47
3.3. SOLUTION APPROACH: HYBRID METAHEURISTIC
Algorithm 2 GRASP RestartRequire: Population Pop, iterations1: BestIndividual← Pop[0]
2: NumRuns←⌈(Popsize−1)
2
⌉3: Clear Population(Pop)4: i← 15: while i ≤ NumRuns do6: j← 17: BestCost←∞8: BestSol← null9: while j ≤ iterations do10: c← Generate Individual with RNN11: Split (c)12: Local Search (c) . Only deterministic costs13: if Cost (c) < BestCost then14: BestCost← Cost (c)15: BestSol← c16: end if17: j ← j + 118: end while19: Local Search (BestSol)20: Add to population (BestSol,Pop)21: i← i+ 122: end while23: Add to population (BestIndividual,Pop)24: Complete Population with Random Individuals25: Sort(Pop)
3.3.3 Crossover
Crossover procedure is performed in order to create new individuals, this is done by means of the OX
crossover. Two individuals p1 and p2 from the population are selected from binary tournaments as
well as two random positions i, j | i 6= j, j > i. Using the permutation representation of solutions,
the information from position i to position j (included) are copied from p1 to the new individual in
the same positions. Additionally, p2 is circularly traversed from position j + 1 to j completing the
ospring from j + 1 to i, with clients in p2 not included yet. By changing the roles of p1 and p2,
another individual can be created using the same procedure. Among the two new chromosomes one
is randomly retained.
3.3.4 Mutation and Local Search
After being created, a new chromosome can be modied by the mutation procedure (line 6 Algorithm
1). It consists in moving nm random selected customers from their current positions to new ones
randomly selected. In our implementation, after preliminary tests the value of nm is set to two. This
operation is performed on the chromosome. The proposed local search (LS) for the MA is organized
as a Variable Neighborhood Descent (VND), a variant of the Variable Neighborhood Search proposed
in [31]. The LS is performed on the routes representation. The neighborhoods used for the VND
(line 8 Algorithm 1) are the Or-opt and 2-opt movements in their intra and inter route versions, and
the inter-CROSS movement. The order in which neighborhoods are explored is randomly selected
each time the LS procedure is called. For a further related review on neighborhoods structures and
types, the reader is referred to [5]. The LS procedure starts by exploring the neighborhoods trying
to improve a solution. Whenever an improving movement is found in a current neighborhood it is
executed, and the procedure passes to the next one when no improving movement is possible. Each
time a solution has been improved, the procedure restarts from the rst neighborhood, running so
on until any of them is able to enhance the solution. Inter route Or-opt movements are limited to
sequences of at most three customers, and inter-cross movements exchange to at most two costumers
48
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
Table 3.1: VRPSD Christiansen and Lysgaard [7] Testbed comparisonMethod
MetricMA +GRASP
GRASP+ HC
SA
Avg. Gap < 0.01% 0.02% 0.35%Max. Gap 0.14% 0.19% 1.89%Avg. Best Gap 0.00% 0.00% 0.04%NBKS 40/40 40/40 33/40Max. CPU (s) 10.13 102.43 603.80Min. CPU (s) 0.69 1.69 9.00Avg. CPU (s) 7.39 36.09 268.66
per route.
3.4 Numerical results
The tests are carried on two groups of instances: the rst group is already used in the VRPSD literature
and is due to Christiansen and Lysgaard [7]. The second one, with larger graphs, is elaborated in this
work to asses the performance of the developed approach on real size benchmarks which can be used
for future works. The detailed results are provided in the following sections.
3.4.1 Classical testbed from Christiansen and Lysgaard
In order to assess the performance of the hybrid MA+GRASP, the tests are carried on 40 existing
instances proposed in [7]. In this benchmark, the number of customers varies from 16 to 60 and the
minimum number of vehicles needed to satisfy the customers demands is comprised between two and
fteen. Christiansen and Lysgaard [7] testbed is based on Augerat test sets A and P and Christodes
and Elion test set E. Demands are assumed to be Poisson distributed as in [7, 20, 30, 15, 29], with
expected values equal to the deterministic demand values. Moreover, travel costs are calculated as the
Euclidean distance between two nodes rounded to the nearest integer as done in [15]. For Christiansen
and Lysgaard [7] benchmark, the best Known Solutions (BKS) are either taken from [15] in which 38
solutions are proven to be optimal, or from [29] and [20] which use heuristic approaches. All tests were
conducted on a Dell Latitude E6420 personal computer with Intel Core i7-2760QM 2.4 GHz, running
under Windows 7 Professional 64 bits. The algorithms were coded on Java and compiled with JavaSE-
1.845 with maximum allocated memory of 1 Gb. Random variables and computation probabilities were
generated by the library of Stochastic Simulation in Java ([25]). Preliminary tests were performed to
select the parameters used by the heuristic, local search rate and mutation probability. These two last
parameters are set to 0.15 and 0.2 respectively. The hybrid MA+GRASP stops after 5000 iterations
without improving the best solution or after running for 10 seconds.
Table 4.1 summarizes the results of the proposed MA+GRASP compared to those of Mendoza et
al. [29] (GRASP-HC), and Goodson et al. [20] obtained with a Simulated Annealing (SA) procedure.
For each method are reported: the average gap on 10 runs (Avg. Gap), the maximum average gap
across the 40 instances (Max Gap), the average gap of the best solution found over the 10 runs (Avg.
Best Gap), the number of best known solutions found considering the 10 runs (NBKS), the average
time over 10 runs for the 40 instances (Avg. CPU), and the maximal and minimal average time over
the 40 instances (Max. CPU Min. CPU).
The MA+GRASP and the GRASP-HC achieve to nd all the BKS (40 out of 40) whereas SA
nd 33 BKS. Moreover, our method reaches an average gap below 0.01% (0.004%) which is nearly
four times lower than the GRASP-HC. The low average gap also shows the stability of the method
when dealing with dierent types of instances. The Max. Gap of 0.14% conrms the ability of the
MA+GRASP method to regularly nd near optimal solutions, this metric shows also that our results
49
3.4. NUMERICAL RESULTS
are better in comparison to all the published methods in the literature. Although times are not scaled
due to dierences in programming languages, operating systems, compiler versions and characteristics
of the computers, MA+GRASP seems to oer the best performance in terms of execution time. The
time metric is specially important in its Max CPU version since the tested instances were small (at
most 60 nodes). The MA+GRASP among the three methods oers the best results quality with
reasonable running times, suggesting it as an ecient method to deal with real life size problems.
Detailed results for MA+GRASP and GRASP+HC are given in table 3.2.
50
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
Table3.2:
ChristiansenandLysgaard[7]testbed
results
MA+GRASP
GRASP
+HC
Instance
BKS
Avg.Cost
BestCost
Avg.Time(s)
Avg.Gap
Avg.Cost
BestCost
Avg.Time(s)
Avg.Gap
A-n32-k5
853.60*
853.60
853.60
5.32
0.00%
853.60
853.60
14.79
0.00%
A-n33-k5
704.20*
704.20
704.20
4.93
0.00%
704.20
704.20
13.15
0.00%
A-n33-k6
793.90*
793.90
793.90
4.77
0.00%
793.90
793.90
13.34
0.00%
A-n34-k5
826.87*
826.87
826.87
5.37
0.00%
826.27
826.87
14.48
0.00%
A-n36-k5
858.71*
858.71
858.71
7.43
0.00%
858.71
858.71
20.26
0.00%
A-n37-k5
708.34*
708.34
708.34
7.47
0.0%
708.34
708.34
23.23
0.00%
A-n37-k6
1030.73*
1030.73
1030.73
6.81
0.00%
1030.86
1030.73
20.14
0.01%
A-n38-k5
775.13*
775.13
775.13
8.08
0.00%
775.13
775.13
20.11
0.00%
A-n39-k5
869.18*
869.18
869.18
8.26
0.00%
869.18
869.18
27.89
0.00%
A-n39-k6
876.60*
876.60
876.60
8.15
0.00%
876.60
876.60
25.33
0.00%
A-n44-k6
1025.48*
1025.48
1025.48
10.10
0.00%
1025.92
1025.48
33.93
0.04%
A-n45-k6
1026.73*
1026.73
1026.73
10.02
0.00%
1026.81
1026.73
31.93
0.01%
A-n45-k7
1264.83*
1264.83
1264.83
10.06
0.00%
1267.05
1264.83
38.47
0.18%
A-n46-k7
1002.22*
1002.22
1002.22
10.10
0.00%
1002.22
1002.22
46.23
0.00%
A-n48-k7
1187.14*
1187.14
1187.14
10.14
0.00%
1187.32
1187.14
55.05
0.02%
A-n53-k7
1124.27*
1124.27
1124.27
10.04
0.00%
1124.27
1124.27
80.22
0.00%
A-n54-k7
1287.07*
1287.07
1287.07
10.09
0.00%
1287.41
1287.07
86.17
0.03%
A-n55-k9
1179.11*
1179.11
1179.11
10.10
0.00%
1179.11
1179.11
66.16
0.00%
A-n60-k9
1529.82
1529.88
1529.82
10.09
0.00%
1529.82
1529.82
102.43
0.02%
E-n22-k4
411.57*
411.57
411.57
1.67
0.00%
411.57
411.57
1.24
0.00%
E-n33-k4
850.27*
850.27
850.27
5.67
0.00%
851.87
850.27
24.66
0.19%
E-n51-k5
552.26
552.26
552.26
10.07
0.00%
552.26
552.26
56.75
0.00%
P-n16-k8
512.82*
512.82
512.82
0.69
0.00%
512.82
512.82
1.69
0.00%
P-n19-k2
224.06*
224.06
224.06
1.72
0.00%
224.06
224.06
3.51
0.00%
51
3.4. NUMERICAL RESULTS
Table3.2:
ChristiansenandLysgaard[7]testbed
results:Continued
MA+GRASP
GRASP
+HC
Instance
BKS
Avg.Cost
BestCost
Avg.Time(s)
AvgGap
Avg.Cost
BestCost
Avg.Time(s)
Avg.Gap
P-n20-k2
233.05*
233.05
631.58
1.86
0.00%
233.05
233.05
4.76
0.00%
P-n21-k2
218.96*
218.96
218.96
2.53
0.00%
218.96
218.96
6.04
0.00%
P-n22-k2
231.26*
231.26
231.26
2.83
0.00%
231.26
231.26
7.10
0.00%
P-n22-k8
681.06*
681.06
681.06
1.18
0.00%
681.06
681.06
4.72
0.00%
P-n23-k8
619.52*
619.53
619.52
1.21
0.00%
619.53
619.52
5.45
0.00%
P-n40-k5
472.50*
472.50
472.50
8.72
0.00%
472.50
472.50
26.49
0.00%
P-n45-k5
533.52*
533.52
533.52
10.03
0.00%
533.83
533.52
36.25
0.06%
P-n50-k10
758.76*
759.04
758.76
10.09
0.03%
758.76
758.76
40.40
0.00%
P-n50-k7
582.37*
582.37
582.37
10.09
0.00%
582.37
582.37
44.17
0.00%
P-n50-k8
669.23*
669.33
669.23
10.05
0.01%
669.33
669.23
39.7
0.02%
P-n51-k10
809.70*
809.70
809.70
10.08
0.00%
809.70
809.70
52.72
0.00%
P-n55-k10
742.41*
742.41
742.41
10.08
0.00%
742.41
742.41
56.26
0.00%
P-n55-k15
1068.05*
1068.05
1068.05
9.38
0.00%
1068.05
1068.05
72.10
0.00%
P-n55-k7
588.56*
588.56
588.56
10.11
0.00%
588.76
588.56
64.54
0.03%
P-n60-k10
803.60*
803.73
803.60
10.06
0.01%
803.60
803.60
73.36
0.00%
P-n60-k15
1085.49*
1087.02
1085.49
10.11
0.14%
1085.49
1085.49
85.52
0.00%
BKScolumn:
Markedwith*whenoptimalproven
52
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
3.4.2 New proposed testbed
To conrm the ability of MA+GRASP to eciently solve the VRPSD, we propose a new testbed of
39 instances composed by the instances of Augerat test sets A and P, which are not considered in [7]
(a total of 22 instances), instances from the Cristodes, Mingozi and Toth CMT test set (4 instances)
and the Rochat and Taillard instances (13 instances). This new benchmark contains from 22 to 385
customers and from 3 to 46 vehicles necessary to satisfy customers demands. The average number
of customers per instance is almost 97. It shall be noticed that some of these have been already
addressed by Gauvin et al. [15]. The proposed testbed has on average nearly 2.4 more customers
than the benchmark of [7] which oers the opportunity for future comparisons of new methods dealing
with the VRPSD. Moreover, travel costs are calculated as the Euclidean distance between two nodes
rounded to the nearest integer as done in [15]
Some adjustments were made for the stopping condition of our hybrid method, for instances with
|V | < 100 the time limit is set to ten seconds, it is increased to 80 seconds for instances with |V | <= 200
and 160 seconds for instances with |V | > 200. Demands are assumed to be Poisson distributed, with
expected values equal to the deterministic demand values. Moreover, travel costs are calculated as
the Euclidean distance between two nodes rounded to the nearest integer.
Table 3.3 presents a summary of the new testbed instances. For each instance is reported: the
number of nodes (|V |), the minimum number of vehicles (Min veh) needed to satisfy the customers
demands, the vehicles capacity (Q), the lling coecient (FC) which stands for∑i∈V E[qi]
Min veh·Q , the best
known solution (BKS) provided either by [15] or among the several preliminary runs and the reported
results, the expected cost of the optimal deterministic solution (BDS)1 in the presence of uncertain
demands2, and the value of the stochastic solution (VSS) which stands for the percentage of improve-
ment among the BDS and the BKS. Furthermore, for the BKS is presented: the number of vehicles
in the BKS (Veh), the total expected cost (Total), the deterministic cost (Det), and the recourse cost
(Rec). The BDS also presents its total expected cost (Total) and its recourse cost (Rec).
As shown in table 3.3 most of the instances (38 out 39) show a positive VSS. Instance E-n23-k3
present a null VSS since the optimal deterministic solution is also optimal in the stochastic scenario.
Overall the VSS rounds the 5.37%, showing the importance of considering the stochasticity. VSS
seems to grow with the number of nodes within each instance, for instances with less than 100 nodes
its average value is 4.67%. Meanwhile, this value increases to 6.39% for instances with more than 100
nodes. As well, instances with more than 150 nodes achieve a VSS of 7.93%. Even if these results are
not conclusive in a direct relation between the VSS and the number of nodes, it seems that the larger
are the instances, the higher is the VSS. Additionally, the BKS cost (Total) is mainly composed by the
deterministic cost averaging 97.38%, while the recourse only achieves 2.66%. Indeed, the positive VSS
can be explained as follows. The BDS is optimal in the deterministic component of the cost. However,
the recourse value of BDS solutions becomes far more important than it is in the BKS. Indeed, the
recourse cost averages 10.88% of the Total BDS in contrast of the 2.55% of the Total BKS. Therefore,
the trade-o between the deterministic and recourse cost is better in the BKS, generating a positive
VSS.
Tables 3.4 and 3.5 illustrate the results on the new set of instances using the MA+GRASP and two
alternative versions, namely MA+RANDOM and NR-MA. MA+RANDOM only changes the way new
individuals are created during the restart procedure, using completely random customers permutations
while NR-MA is a simpler version of the algorithm without considering the restart procedure. For
each instance on this testbed are provided in table 3.4: the best known solution (BKS) with the same
considerations as in table 3.3, the best cost found out on the 10 runs (Best Cost) and the average cost
1Optimal deterministic solutions retrieved from http://vrp.atd-lab.inf.puc-rio.br or https://www.coin-or.org/SYMPHONY/branchandcut/VRP/data/index.htm for all instances except tai385 for which the deterministicBKS from the rst link is used.
2We use the same approach of Gauvin et al. [15], by evaluating the routes in the optimal deterministic solution tothe right and the reverse, retaining the one with the minimum cost
53
3.4. NUMERICAL RESULTS
Table 3.3: Summary of new testbedBKS BDS
Instance |V | Min veh Q FC Veh Total Det Rec Total Rec VSSA-n61-k9 61 9 100 0.98 10 1144.23 1084 60.23 1215.37 181.37 5.85%A-n62-k8 62 8 100 0.92 9 1430.81 1375 55.81 1533.07 245.07 6.67%A-n63-k10 63 10 100 0.93 11 1459.49 1412 47.49 1581.17 267.17 7.70%A-n63-k9 63 9 100 0.97 10 1847.69 1734 113.69 1991.75 375.75 7.23%A-n64-k9 64 9 100 0.94 10 1569.85 1534 35.85 1699.68 298.68 7.64%A-n65-k9 65 9 100 0.97 10 1313.30 1266 47.30 1421.52 247.52 7.61%A-n69-k9 69 9 100 0.94 10 1259.35 1214 45.35 1339.55 180.55 5.99%A-n80-k10 80 10 100 0.94 11 1987.17 1918 69.17 2109.56 346.56 5.80%E-n23-k3 23 3 4500 0.75 3 569.72 569 0.72 569.72 0.72 0.00%E-n30-k3 30 3 4500 0.94 4 504.55 503 1.55 569.92 32.92 11.47%E-n76-k10 76 10 140 0.97 11 885.11 861 24.11 911.70 81.70 2.92%E-n76-k14 76 14 100 0.97 16 1118.90 1086 32.90 1187.18 166.18 5.75%E-n76-k7 76 7 220 0.89 7 698.95 692 6.95 723.96 41.96 3.46%E-n76-k8 76 8 180 0.95 8 771.23 744 27.23 791.88 56.88 2.61%E-n101-k14 101 14 112 0.93 15 1164.149 1116 48.15 1233.84 166.84 5.65%E-n101-k8 101 8 200 0.91 8 839.47 824 15.47 878.36 63.36 4.43%P-n55-k8 55 7 160 0.93 7 607.71 581 26.71 631.82 43.82 3.82%P-n65-k10 65 10 130 0.94 10 854.06 802 52.06 861.52 69.52 0.87%P-n70-k10 70 10 135 0.97 11 882.01 851 31.01 929.82 102.82 5.14%P-n76-k4 76 4 350 0.97 4 609.54 593 16.54 609.62 16.62 0.01%P-n76-k5 76 5 280 0.97 5 648.11 628 20.11 649.10 22.10 0.15%P-n101-k4 101 4 400 0.91 4 686.81 684 2.81 694.47 13.47 1.10%CMT12 101 10 200 0.91 10 982.80 827 155.81 984.31 164.31 0.15%CMT11 121 7 200 0.98 8 1201.15 1187 14.15 1226.62 189.62 2.08%CMT4 151 12 200 0.93 12 1072.19 1036 36.19 1130.31 114.31 5.02%CMT5 200 16 200 1.00 18 1378.85 1355 23.85 1486.96 209.96 7.27%Tai75a 76 10 1445 0.95 11 1653.85 1649 4.85 1718.39 102.39 3.76%Tai75b 76 9 1679 0.99 10 1353.09 1343 10.09 1438.62 99.62 5.95%Tai75c 76 9 1122 0.94 9 1349.87 1332 17.87 1396.98 110.98 3.37%Tai75d 76 9 1699 0.93 9 1392.86 1391 1.86 1444.39 87.39 3.57%Tai100a 101 11 1409 0.98 12 2107.71 2089 18.71 2388.31 350.31 11.75%Tai100b 101 11 1842 0.96 12 1985.33 1986 29.33 2118.93 181.93 6.31%Tai100c 101 11 2043 0.93 11 1421.66 1413 8.66 1526.73 126.73 6.88%Tai100d 101 11 1297 0.95 12 1602.53 1599 3.53 1748.94 174.94 8.37%Tai150a 151 15 1544 0.94 15 3211.55 3188 23.55 3574.95 530.95 10.10%Tai150b 151 14 1918 0.95 15 2792.39 2789 3.39 3113.52 397.52 10.31%Tai150c 151 14 2021 0.99 15 2406.13 2380 26.13 2546.29 201.29 5.50%Tai150d 151 14 1874 0.97 15 2718.39 2692 26.39 3044.91 406.91 10.64%Tai385 386 46 65 1.00 55 29364.03 28007 1357.03 31360.45 7008.45 6.22%
(Avg. Cost). Additionally, in table 3.5 are reported: the computational time (Time) in seconds, the
gap between the average cost and the BKS (Avg. Gap) and the gap between the best solution found
and the BKS (Best Gap). Moreover, ten out of the 39 instances have a proven optimal solution given
by Gauvin et al. [15]3, these values are marked with an asterisk in table 3.4.
Overall, the MA+GRASP shows a good performance presenting 33 out of 39 instances with an
average gap bellow one percent. Furthermore, the average gap is as low as 0.380%. MA-RANDOM
ranks second in this metric with 0.418% and the NR-MA achieves 0.434%. MA+GRASP presents an
average gap of 0.64% for instances with 100 customers or more, while instances with fewer customers
show an average gap of 0.20%. The dierence can be explained by the inherent combinatorial nature of
the problems at hand. MA+GRASP accomplishes to nd nine out of ten proven optimal solutions, still
instance P-n65-k10 is the only one which cannot be found by this method. Furthermore, MA+GRASP
as well as NR-MA reach 22 of the BKS while MA+RANDOM nds 21 BKS. In addition, the Best
gap metric averages only 0.16% with a maximum of 1.21% in instance CMT5 for the MA+GRASP.
MA+RANDOM and NR-MA perform similarly with an average best gap of 0.19%. Computational
times present a similar behavior, the MA+GRASP uses 40.92 seconds on average to solve each instance,
MA+RANDOM uses slightly less time with an average of 40.43 seconds, while the NR-MA reaches
the lowest value with 39.81 seconds. Although computational times cannot be directly compared to
those of table 4.1, obtained results suggest that our method has competitive times even for larger
instances.
3Instance CMT12 appears in Gauvin et al. [15] as M-n101-k10
54
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
Table3.4:
VRPSD
Proposed
testbed
results-Costs
BKS
MA+GRASP
MA+RANDOM
NR-M
A
Instance
BestCost
Avg.Cost
BestCost
AvgCost
BestCost
AvgCost
A-n61-k9
1144.23
1144.23
1144.72
1144.23
1146.00
1144.23
1146.37
A-n62-k8
1430.81*
1430.81
1431.36
1430.81
1431.56
1430.81
1431.05
A-n63-k10
1459.49*
1459.49
1459.49
1459.49
1459.57
1459.49
1459.78
A-n63-k9
1847.69
1848.69
1851.74
1852.12
1852.21
1851.23
1852.28
A-n64-k9
1569.85
1569.85
1570.07
1569.85
1570.07
1569.85
1570.07
A-n65-k9
1313.30*
1313.30
1313.30
1313.30
1313.30
1313.30
1313.30
A-n69-k9
1259.35*
1259.35
1259.81
1259.35
1259.95
1259.35
1259.37
A-n80-k10
1987.17
1987.17
1993.07
1987.17
1995.33
1988.25
1994.28
E-n23-k3
569.72*
569.72
569.72
569.72
569.72
569.72
569.72
E-n30-k3
504.55*
504.55
504.55
504.55
504.55
504.55
504.55
E-n76-k10
885.11*
885.11
886.77
885.11
889.03
885.11
887.71
E-n76-k14
1118.90
1118.90
1120.34
1118.90
1121.03
1118.90
1119.52
E-n76-k7
698.95
698.95
699.30
698.95
698.95
698.95
699.06
E-n76-k8
771.23
771.94
774.48
771.94
773.64
771.94
774.52
E-n101-k8
839.47
839.47
840.37
839.47
839.89
839.47
840.12
E-n101-k14
1164.15
1164.15
1167.61
1164.15
1170.72
1164.15
1170.38
P-n55-k8
607.71
607.71
607.71
607.71
607.71
607.71
607.71
P-n65-k10
854.06*
858.30
859.57
859.17
859.71
858.30
859.43
P-n70-k10
882.01*
882.01
883.20
882.01
883.83
882.01
884.37
P-n76-k4
609.54
611.59
616.56
614.04
616.44
614.04
616.48
P-n76-k5
648.11
652.16
652.71
652.16
653.80
652.16
653.02
P-n101-k4
686.81
686.81
687.78
687.67
688.57
686.81
687.91
CMT12
982.80*
982.80
982.80
982.80
982.80
982.80
982.80
CMT11
1201.15
1202.26
1208.66
1202.75
1207.03
1202.98
1208.14
55
3.4. NUMERICAL RESULTS
Table3.4:
VRPSD
Proposed
testbed
results-Costs:Continued
BKS
MA+GRASP
MA+RANDOM
NR-M
A
Instance
BestCost
Avg.Cost
BestCost
AvgCost
BestCost
AvgCost
CMT4
1072.19
1076.84
1084.79
1082.70
1085.47
1082.55
1088.21
CMT5
1378.85
1395.52
1404.44
1391.27
1400.47
1388.90
1399.96
Tai75a
1653.85
1653.85
1654.30
1653.85
1654.30
1653.85
1653.86
Tai75b
1353.09
1353.09
1355.74
1353.09
1355.93
1353.09
1357.38
Tai75c
1349.87
1354.42
1354.55
1354.42
1354.55
1354.42
1357.47
Tai75d
1392.86
1392.86
1393.15
1392.86
1393.19
1393.20
1393.22
Tai100a
2107.71
2109.39
2112.72
2113.50
2121.39
2111.09
2120.53
Tai100b
1985.33
1988.67
1991.05
1988.67
1990.84
1988.67
1990.14
Tai100c
1421.66
1422.54
1422.84
1422.75
1423.38
1421.66
1423.05
Tai100d
1602.53
1602.53
1603.19
1602.53
1602.62
1602.53
1602.53
Tai150a
3211.55
3243.06
3257.09
3225.34
3257.29
3239.82
3255.23
Tai150b
2792.39
2794.43
2810.26
2792.51
2807.16
2792.40
2803.75
Tai150c
2406.13
2410.29
2416.04
2407.62
2423.69
2407.58
2423.64
Tai150d
2718.39
2721.86
2755.41
2731.58
2758.45
2738.25
2777.99
Tai385
29364.03
29597.42
29798.48
29632.52
29828.55
29582.63
29827.15
Average
2074.41
2081.2
2089.7
2082.
12
2091.
12081.2
2091.4
Max
29364.0
2959
7.0
29798.5
29632.
529828.
629582.6
29827.2
Min
504.6
504.6
504.6
504.
6504.
6504.6
504.6
BKScolumn:Marked
with*when
optimalproven.BestCostcolumns:bold
valueifBKSfound
56
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
Table3.5:
VRPSD
Proposed
testbed
results-Time,Gaps
MA+GRASP
MA+RANDOM
NR-M
A
Instance
Time(s)
Avg.Gap
BestGap
Time(s)
Avg.Gap
BestGap
Time(s)
Avg.Gap
BestGap
A-n61-k9
10.12
0.04%
0.00%
10.03
0.15%
0.00%
10.02
0.19%
0.00%
A-n62-k8
10.05
0.04%
0.00%
10.02
0.05%
0.00%
10.02
0.02%
0.00%
A-n63-k10
10.17
0.00%
0.00%
10.02
0.01%
0.00%
10.01
0.02%
0.00%
A-n63-k9
10.02
0.22%
0.05%
10.01
0.24%
0.24%
10.01
0.25%
0.19%
A-n64-k9
10.36
0.01%
0.00%
10.02
0.01%
0.00%
10.02
0.01%
0.00%
A-n65-k9
10.35
0.00%
0.00%
10.02
0.00%
0.00%
10.01
0.00%
0.00%
A-n69-k9
10.42
0.04%
0.00%
10.03
0.05%
0.00%
10.02
0.00%
0.00%
A-n80-k10
10.17
0.30%
0.00%
10.04
0.41%
0.00%
10.05
0.36%
0.05%
E-n23-k3
2.84
0.00%
0.00%
2.21
0.00%
0.00%
2.23
0.00%
0.00%
E-n30-k3
5.90
0.00%
0.00%
4.49
0.00%
0.00%
4.18
0.00%
0.00%
E-n76-k10
10.15
0.19%
0.00%
10.03
0.44%
0.00%
10.02
0.29%
0.00%
E-n76-k14
10.12
0.13%
0.00%
10.02
0.19%
0.00%
10.01
0.06%
0.00%
E-n76-k7
10.51
0.05%
0.00%
10.06
0.00%
0.00%
10.02
0.02%
0.00%
E-n76-k8
10.16
0.42%
0.09%
10.03
0.31%
0.09%
10.02
0.43%
0.09%
E-n101-k8
80.18
0.11%
0.00%
80.18
0.05%
0.00%
80.07
0.08%
0.00%
E-n101-k14
80.22
0.30%
0.00%
76.74
0.56%
0.00%
64.30
0.54%
0.00%
P-n55-k8
10.14
0.00%
0.00%
10.01
0.00%
0.00%
10.01
0.00%
0.00%
P-n65-k10
10.20
0.64%
0.50%
10.01
0.66%
0.60%
10.01
0.63%
0.50%
P-n70-k10
10.29
0.13%
0.00%
10.02
0.21%
0.00%
10.01
0.27%
0.00%
P-n76-k4
10.22
1.15%
0.34%
10.04
1.13%
0.74%
10.05
1.14%
0.74%
P-n76-k5
10.20
0.71%
0.62%
10.07
0.88%
0.62%
10.03
0.76%
0.62%
P-n101-k4
80.59
0.14%
0.00%
80.16
0.26%
0.12%
80.19
0.16%
0.00%
CMT12
80.69
0.00%
0.00%
79.38
0.00%
0.00%
71.74
0.00%
0.00%
CMT11
80.16
0.63%
0.09%
80.22
0.49%
0.13%
80.25
0.58%
0.15%
57
3.4. NUMERICAL RESULTS
Table3.5:
VRPSD
Proposed
testbed
resultsTime,Gaps:Continued
MA+GRASP
MA+RANDOM
NR-M
A
Instance
Time(s)
Avg.Gap
BestGap
Time(s)
Avg.Gap
BestGap
Time(s)
Avg.Gap
BestGap
CMT4
80.65
1.18%
0.43%
80.39
1.24%
0.98%
80.24
1.49%
0.97%
CMT5
80.53
1.86%
1.21%
80.54
1.57%
0.90%
80.46
1.53%
0.73%
Tai75a
10.24
0.03%
0.00%
10.02
0.03%
0.00%
10.03
0.00%
0.00%
Tai75b
10.04
0.20%
0.00%
10.02
0.21%
0.00%
10.25
0.32%
0.00%
Tai75c
10.18
0.35%
0.34%
10.03
0.35%
0.34%
10.02
0.56%
0.34%
Tai75d
10.19
0.02%
0.00%
10.03
0.02%
0.00%
10.04
0.03%
0.02%
Tai100a
80.24
0.24%
0.08%
79.48
0.65%
0.27%
78.00
0.61%
0.16%
Tai100b
80.92
0.29%
0.17%
77.57
0.28%
0.17%
75.76
0.24%
0.17%
Tai100c
80.41
0.08%
0.06%
78.71
0.12%
0.08%
79.99
0.10%
0.00%
Tai100d
80.62
0.04%
0.00%
80.08
0.01%
0.00%
80.06
0.00%
0.00%
Tai150a
80.19
1.42%
0.98%
80.27
1.42%
0.43%
80.19
1.36%
0.88%
Tai150b
80.93
0.64%
0.07%
80.16
0.53%
0.00%
80.19
0.41%
0.00%
Tai150c
81.15
0.41%
0.17%
80.16
0.73%
0.06%
80.25
0.73%
0.06%
Tai150d
81.09
1.36%
0.13%
80.31
1.47%
0.48%
80.20
2.19%
0.73%
Tai385
164.64
1.48%
0.79%
165.52
1.58%
0.91%
163.91
1.58%
0.74%
Average
40.93
0.38
0%0.
16%
40.44
0.418%
0.18%
39.8
20.4
34%
0.18%
Max
164.64
1.86
%1.
21%
16552
1.57%
0.98%
163.9
12.1
9%
0.97%
Min
2.84
0.00
%0.
00%
2.21
0.00%
0.00%
2.2
30.0
0%
0.00%
58
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
0,00%
0,20%
0,40%
0,60%
0,80%
1,00%
1,20%
1,40%
1,60%
1,80%
2,00%
0,88 0,90 0,92 0,94 0,96 0,98 1,00
Avg
. Gap
-M
A+
GR
ASP
Filling capacity
0,00%
0,20%
0,40%
0,60%
0,80%
1,00%
1,20%
1,40%
1,60%
1,80%
2,00%
0,65 0,70 0,75 0,80 0,85 0,90 0,95 1,00
Avg
. Gap
-M
A+G
RA
SP
Filling capacity* - BKS number of vehicles
Figure 3.3: Avg. gap of MA+GRASP against the lling coecient
General performance discussion
Authors like Laporte et al. [24] or Jabali et al. [22] had reported the diculty of their exact ap-
proaches to solve the VRPSD when the number of vehicles increases or when the lling coecient
(FC) approaches one. Although in our approach the eet is considered unlimited instead of xed,
we test if such parameters had an impact on our solution method. We use as proxy the average gap
retrieved by the MA+GRASP to compare it against both the FC and the number of vehicles. Figure
3.3 presents the relation between the FC and the average gap. In the left gure the FC reported in
table 3.3 is used while the right part represents a FC calculated using the actual number of vehicles
used in the BKS. Instance E-n23-k3 was discarded for the graphs as far as this point is a very extreme
point4. The graph with the original FC shows a slightly positive trend between the variables while
the modied FC presents no relation at all.
The same approach to FC is used to compare the avg gap of MA+GRASP with the number
of vehicles used in the BKS. Figure 3.4 presents the graph of both variables. The graph uses the
information of all instances but Tai385 which also is marked as an extreme point when compared to
the rest of the testbed5. A clearer pattern of a positive relation between the variables is devised for
this relation. Nevertheless, a further inspection on gure 3.5 shows that the number of vehicles is
highly correlated with the number of nodes per instance. Moreover, this same gure shows a very
clear pattern in the relation of the average gap against the number of nodes in the instance. The
results are not surprising as far as this parameter has an important inuence on the speed of the local
searches of the MA+GRASP and is of importance in the framework of the MA. Consequently, new
methods relying on local search procedures should aim to minimize the complexity of the task.
Addressing the eect of GRASP restart
In order to test the impact of the GRASP and the restart procedure included in the MA, the variance
analysis method of Friedman is used to compare the MA+GRASP against the MA+RANDOM. The
results presented in table 3.4 are intended to perform two comparisons in terms of the best solution
found, and the average cost. The Friedman tests works as follows. For each methods comparison,
results are ranked within each instance giving one to the best value to two to the worst one, average
values are used in case of ties (table 3.7). Let R (Xij) be the rank of the heuristic j in instance i,
b the number of instances, k the number of tested heuristics and Rj =∑bi=1R (Xij)∀j). Moreover,
A =∑bi=1
∑kj=1 (R (Xij))
2, C = bk(k+1)2
4 , T 1 =(k−1)
∑kj=1(Rj−
b(k+1)2 )2
A−C , T 2 = (b−1)T 1
b(k+1)−T 1. In addition
t1−(α2 ) stands for the 1 − α2 quantile of the Student t distribution with (b− 1) (k − 1) degrees of
freedom and F1−(α2 ) the 1 − α2 quantile of the F distribution with (k − 1) numerator degrees of
freedom and (b − 1)(k − 1) denominator degrees of freedom. The test to set if the heuristics present
4E-n23-k3 instance has a FC which is nearly 5 standard deviations from the mean FC5Tai385 has a number of vehicles in the BKS which is over 5 standard deviations from the mean
59
3.4. NUMERICAL RESULTS
0,00%
0,20%
0,40%
0,60%
0,80%
1,00%
1,20%
1,40%
1,60%
1,80%
2,00%
0 5 10 15 20
Avg
. Gap
-M
A+G
RA
SP
Number of vehicles in BKS
Figure 3.4: Avg. gap of MA+GRASP against the number of vehicles in the BKS
0,00%
0,20%
0,40%
0,60%
0,80%
1,00%
1,20%
1,40%
1,60%
1,80%
2,00%
0 50 100 150 200
Avg
. Gap
-M
A+G
RA
SP
Number of nodes
0
2
4
6
8
10
12
14
16
18
20
0 50 100 150 200
Nu
mb
er o
f ve
hic
les
in B
KS
Number of nodes
Figure 3.5: Avg. gap of MA+GRASP and number of vehicles against the number nodes per instance
a dierence within a metric is dened as:
T 2 > F 1−(α2 ) (3.4)
By setting α = 1% the right side of the equation 4.4 becomes 7.35 for every comparison, the left side
is calculated with the values presented in table 3.7 giving 8.05, and 4.34 for the average cost, and the
best cost respectively. That means, that there exist statistically dierences between the methods for
the average cost. If α = 5% then the right side of the equation 4.4 becomes 4.09 and thus at 5% level
of signicance there exist statistically dierences in the best cost metric. Provided that the methods
have dierent performances, the test to set if two heuristics are statistically dierent within a metric
can be expressed as follows:
|Rl −Rm| > t1−(α2 )
√√√√2(bA−
∑kj=1Rj
2)
(b− 1) (k − 1)(3.5)
Let R1, R2 be the Rj metrics for the MA+GRASP, and MA+RANDOM respectively. Table 4.5
summarizes the necessary values for performing the tow two-pair comparisons. Moreover, the right
side of equation 4.5 has values of 14.33 and 7.77 provided that α = 1% for the average cost and
α = 5% for the best cost metric. As far as equation 4.5 holds for every |Rl −Rm| we can conclude the
statistical dierence the heuristics within each metric and therefore, MA+GRASP can be determined
as the best alternative.
Indeed, results show that the MA+GRASP presents the best performance and it is statistically
signicant on the average cost at a condence level of α = 1%, and for the best solution found at
60
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
Table 3.6: Two-way test values for Friedman methodMetric
AverageCost
Best Cost
|R1 −R2| 15.0 8.0
Table 3.7: Friedman test ranksMA+GRASP MA+RANDOM
InstanceAvg. Cost
rank
Best Cost
rank
Avg. Cost
rank
Best Cost
rank
A-n61-k9 1 1 2 2A-n62-k8 1 1.5 2 1.5A-n63-k10 1 1.5 2 1.5A-n63-k9 1 1 2 2A-n64-k9 1.5 1.5 1.5 1.5A-n65-k9 1 1.5 2 1.5A-n69-k9 1 1.5 2 1.5A-n80-k10 1 1.5 2 1.5CMT12 1.5 1.5 1.5 1.5CMT11 2 1 1 2CMT4 1 1 2 2CMT5 2 2 1 1E-n101-k14 1 1.5 2 1.5E-n101-k8 2 1.5 1 1.5E-n23-k3 1.5 1.5 1.5 1.5E-n30-k3 1.5 1.5 1.5 1.5E-n76-k10 1 1.5 2 1.5E-n76-k14 1 1.5 2 1.5E-n76-k7 2 1.5 1 1.5E-n76-k8 2 1.5 1 1.5P-n101-k4 1 1 2 2P-n55-k8 1.5 1.5 1.5 1.5P-n65-k10 1 1 2 2P-n70-k10 1 1.5 2 1.5P-n76-k4 2 1 1 2P-n76-k5 1 1.5 2 1.5Tai100a 1 1 2 2Tai100b 2 1.5 1 1.5Tai100c 1 1 2 2Tai100d 2 1.5 1 1.5Tai150a 1 2 2 1Tai150b 2 2 1 1Tai150c 1 2 2 1Tai150d 1 1 2 2Tai75a 1 1 2 2Tai75b 1 1.5 2 1.5Tai75c 1.5 1.5 1.5 1.5Tai75d 1 1.5 2 1.5Tai385 1 1 2 2Rj 51.0 54.5 66.0 62.5
61
3.4. NUMERICAL RESULTS
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
0 10 20 30 40 50 60 70 80
Cu
mu
lati
ve p
rob
abili
ty
Time (seconds)
MTTT Plot - Target Value 5% gap to BKS
MA+GRASP NR - MA MA+RANDOM
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Cu
mu
lati
ve p
rob
abili
ty
Time (seconds)
MTTT Plot - Target Value 1% gap to BKS
MA+GRASP NR - MA MA+RANDOM
Figure 3.6: MTTT Plots for the dierent MA methods - 5% and 1%
α = 5%. Overall MA+GRASP presents the best quality results, and the Friedman tests conrms
the enhancement of the method with the restart procedure driven by a GRASP. Besides, a multiple
instances and targets time-to-target plot (mttt-plot) was used to compare the three solution versions.
The mttt-plot method is an extension of the time-to-target plots (ttt-plots) introduced in [14]. Ac-
cording to Reyes and Ribeiro [34] the plots can be used to compare the running times of stochastic
algorithms or dierent strategies for solving a given problem. The approach is based on the construc-
tion by simulation presented in [34]. The resulting graphs represents the probability of nding the
proposed target values (one per instance) for the set of instances in a specic amount of time.
To extract the necessary information, we run again the three methods for each of the new testbed
instances. The stopping criteria is set to a maximum time of half an hour or when a BKS is achieved.
During the execution time, for each instance run and method, the best solution is always surveyed, so
when a new best solution is found the necessary time to nd it is recorded. Ten runs are performed
per instance and per method. Using this information we estimate the probability distribution of the
time required to nd dierent target values. Indeed, we vary the target value for each instance as a
function of a gap to the BKS reported in table 3.36. This allows to see how the methods perform over
dierent targets giving a more comprehensive story of the performance of the algorithms.
As far as we imposed a limit time, it may happen that a method cannot meet the required target.
In such case we let the maximum time as record. The rationale behind this is that even if we do
not know the actual time required to nd the target value, it stills contains valuable information.
Indeed, more stable methods should be able to nd the BKS (or very near BKS) targets more often.
Therefore, letting the maximum time as record when the target value is not found accomplishes the
aim of characterizing the solution method times. Moreover, this allows to limit the computational
times required to perform a mttt-plot analysis, as far as some methods can require huge amount of
time to nd certain targets for some instances.
Figure 3.6 shows the mttt-plots for target values not farther than 5% and 1% of the BKS. The
5% gure concludes that the NR-MA is better than MA+RANDOM and MA+GRASP, and with less
time it has higher probabilities of achieving a 5% gap to BKS for the whole benchmark of instances.
Nevertheless, the 1% gure displays a completely dierent behavior. In this last, the MA+RANDOM
and MA+GRASP present a very similar behavior which is far better than NR-MA. For example, the
probability of nding a target value within 1% of the BKS (for the whole set of instances) within 2500
seconds is one for MA+GRASP and MA+RANDOM, for NR-MA is around 70%.
Going further to determine the best method, gure 3.7 presents the mttt-plots for target val-
ues of 0.5% and 0% of the BKS. In both targets it is shown that MA+GRASP outperforms the
MA+RANDOM and the NR-MA showing a higher probability of nding the matches for a xed
amount of time. It is interesting that as the target approaches the BKS the performance of MA+RANDOM
6Except for instances CMT4, Tai 150a, 150c, 150d, and 385 for which the BKS reported was found during this longrun, a slightly bigger BKS was used during this tests
62
CHAPTER 3. HYBRID METAHEURISTIC FOR THE VRPSD
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
0 2000 4000 6000 8000 10000 12000 14000 16000
Cu
mu
lati
ve p
rob
abili
ty
Time (seconds)
MTTT Plot - Target Value 0.5% gap to BKS
MA+GRASP NR - MA MA+RANDOM
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
0 5000 10000 15000 20000 25000 30000 35000 40000
Cu
mu
lati
ve p
rob
abili
ty
Time (seconds)
MTTT Plot - Target Value 0% gap to BKS
MA+GRASP NR - MA MA+RANDOM
Figure 3.7: MTTT Plots for the dierent MA methods - 0.5% and 0%
and NR-MA become very similar. This shows that the random restart might have a very little impact
on the solution approach in long runs. However, the restart based on the GRASP does show an
important impact, giving the edge to MA+GRASP as the best option among the tested ones to solve
the VRPSD.
3.5 Conclusions
In this chapter the Vehicle Routing Problem with Stochastic Demands (VRPSD) was studied. In order
to solve this problem a MA+GRASP method is proposed. The obtained results on a classical testbed
from Christiansen and Lysgaard [7] show that our method outperforms state-of-the-art algorithms in
terms of quality and eciency. Moreover a new testbed with nearly 2.4 more customers on average per
instance is derived. The results on this new benchmark conrm the pertinence of the MA+GRASP
which can eciently solve instances with as much as 385 customers. The new instances, which are
closer to real life size problems, and the presented results can be used for future comparison. Research
currently underway includes the evaluation of new recourse actions, the extension of the problem to
consider other stochastic parameters in order to solve problems closer to reality and the introduction of
correlation among the demands. Finally, undergoing work also search the means to combine heuristic
methods with exact algorithms to tackle large instances of dierent SVRPs.
Contributions
Preliminary results of this chapter were presented at CIE45 conference:
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2015)
A Memetic Algorithm for the Vehicle Routing Problem with Stochastic Demands
In Proceedings of the 45th International Conference on Computers & Industrial Engineering CIE45
Metz, France, 2830 October, 2015.
An article version of this chapter has been published in the Computers & Operations Research
journal. Please cite it as follows:
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2018). A Hybrid metaheuristic algorithm for
the vehicle routing problem with stochastic demands. Computers & Operations Research, 99, 135-147.
https://doi.org/10.1016/j.cor.2018.06.012.
63
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66
Chapter 4
Vehicle Routing Problem with
stochastic travel and service times
4.1 Introduction
The Vehicle Routing Problem (VRP) is one of the most studied combinatorial problems; its importance
is inherent in the number of real applications where it arises, as well as its signicant theoretical
contributions to combinatorial optimization eld. First proposed by Dantzig and Ramser [10], many
variants of the problem has been considered, e.g. Vehicle Routing Problem with Time Windows
(VRPTW), Distance Constrained Vehicle Routing Problem (DVRP), Orienteering Problem, among
others. Each of the proposed variants aims to address the problem in a more realistic way to nd more
reliable solutions in real world applications. Nevertheless, one of the most important drawbacks in
classical models is that available information can be limited because many of the input parameters are
uncertain, such as travel times, service times, customers demands, or customer presence. Neglecting
the lack of complete information can conduce to, for example, suboptimal solutions, so implying
higher operational costs or customer dissatisfaction. Stochastic Vehicle Routing Problems (SVRP)
arise when parameters are modeled as random variables, making models closer to reality but harder
to solve than their deterministic counterpart.
SVRP are usually solved by means of stochastic programming. Two approaches are often used
to model and solve a stochastic optimization problem: Chance Constrained Programming (CCP)
and Stochastic Programming with Recourse (SPR). CCP aims to solve the problem by bounding the
probability of constraints violations to a threshold. SPR uses recourse which are actions to recover
the feasibility of the solution when failures occur. The expected costs related to these actions are
taken into account in the objective function. Both approaches (CCP and SPR) rely on a two-stage
approach: in the rst stage a priori solution is created and then at the second stage the parameters
are revealed. However, since CCP does not consider the cost associated with failures, the quality of
the solutions might be inferior to those provided by SPR models.
This chapter deals with maintenance scheduling and routing problems where groups of technicians
must be assigned to visit a set of customers to execute repairing tasks within given time windows.
The goal is to build a minimum cost set of routes subject to the following constraints: every route
starts and ends at the depot; each customer is visited and serviced once by only one route within its
time window, and the sum of customer demands on each route does not exceed the vehicle capacity.
With regard to time window constraints, there are two cases that are encountered in the literature:
hard time windows exist when the service cannot start outside the time interval; early arrivals are
permitted but the vehicle must wait until the opening of the time window. In contrast, soft time
windows services are allowed outside the time windows usually with a penalty cost. In this study, the
67
4.1. INTRODUCTION
vehicle routing problem with hard time windows and stochastic travel and service times (SVRPTW)
is considered. In fact, travel times modeled as random variables consider the uncertainty in time due
to trac, weather, accidents, driving skills, etc. Furthermore, the stochastic service time accounts for
the complexity of maintenance tasks, which may only be known when technicians arrive at customer
locations.
A recent survey on SVRPs is presented in Gendreau et al. [16] reviewing dierent variants,
e.g. stochastic demands, customers, travel and service times. The version with stochastic demands
(VRPSD) was rst proposed by Tillman [45] with later works on exact methods in [28, 8, 15] and
heuristic methods in [42, 19, 33]. A VRPSD where the presence of customers is also a random variable
is presented by Gendreau et al. [17] and solved with a Tabu Search.
Earlier related works concerning stochastic travel and/or times problems include those of Laporte
et al. [27] for the Vehicle Routing Problem with Stochastic Travel and Service times, in which the
authors propose three models, including a CCP and an SPR, solving instances with up to 20 nodes
where travel times are restricted to ve discrete states. Lambert et al. [26] propose an adaptation of
the Clarke and Wright [9] saving heuristic to solve the VRP with stochastic travel times for money
collection. Later, two models for the VRP with stochastic travel and service times are presented in
Kenyon and Morton [24]. The rst minimizes the expected completion time, while the second model
maximizes the probability of completion within a pre-specied deadline. To solve small instances, the
authors used a branch-and-cut algorithm; for larger ones, the branch-and-cut scheme was embedded
in a Monte Carlo sampling approach.
A Genetic Algorithm reporting signicant improvements in terms of costs and delay penalties is
referred to in [2] for the SVRP with time windows and stochastic travel times. More recently, the
SVRP with stochastic travel and service times is modeled by Li et al. [31] as a CCP and an SPR.
The problem is solved using a Tabu Search, and Monte Carlo simulation is used to check stochastic
constraint feasibility and estimate the expected recourse value. The authors test this approach on
their own instances with up to 100 clients and soft time windows, concluding that CCP models are
harder to solve than SPR. Zhang et al. [49] also address the SVRP with stochastic travel and service
times with soft time windows. The authors propose a CCP model to guarantee a service level to
customers services, arrival at the depot and on the total duration of the routes. To estimate the
vehicles arrival times, a discrete approximation method is embedded into a Tabu search heuristic to
solve some Solomon [41] instances with up to 20 customers. A Tabu Search is also proposed in Ta³ et
al. [43] for the SVRP with stochastic travel times and soft time windows. The objective is to minimize
a weighted cost composed of two parts: the rst relates to the service (early and late arrivals) and
the second to transportation (distance, xed vehicle costs and overtime). Ta³ et al. [44] analyze the
same problem but deal with stochastic time-dependent travel times. The authors deduced a way of
calculating exactly the mean and variance of arrival times provided that service times for customers
equal zero. Conversely, when service times are non-zero, an approximation is used. Both, exact and
approximation schemes are derived if travel times are Gamma distributed.
A Satiscing Measure Approach (SMA) to mitigate the dissatisfaction experienced by customers
is proposed in Nguyen et al. [39]. The SMA is realized for both the SVRP with stochastic demands
and time windows and the SVRP with stochastic travel times and time windows. The latter model
considers the early time window (ready time) as a strict requirement and the late time window (due
time) as a soft requirement. The results for Solomon [41] instances show small computational times.
Errico et al. [13] eectively solve the SVRP with hard time windows and stochastic service times
with a branch-cut-and-price algorithm for instances with up to 50 customers. The model proposed
by the authors is an SPR, nevertheless two conditions (constraints) are imposed: a service level on
being operationally feasible for each route is required and a maximum of one recourse per route is
allowed. A variant of the problem that considers multiple depots and client priorities is addressed by
Binart et al. [4]. The problem incorporates two types of customers: optional and mandatory. The
68
CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
former customers have an associated hard time window that must be respected. The problem is solved
with a two-stage method and is proved eective in instances containing up to 50 customers and three
vehicles.
Jula et al. [22] develop approximations for the mean and variance of the arrival times for the
Traveling Salesman Problem with time windows (TSPTW) which is solved via dynamic program-
ming. The arrival times mean and variance are estimated using a rst-order Taylors series expansion.
The authors guarantee a service level requirement on clients time windows using the Chebyshev and
Cherno bounds. In the same vein, Ehmke et al. [11] estimate both the arrival and service times for
the VRP with stochastic travel times and time windows using a normal approximation. The authors
embed their approximation within a Tabu Search method to solve a CCP formulation on Solomon
[41] instances where late services are allowed. The work of Gomez et al. [18] tackles the estimation of
arrival times using phase-type (PH) distribution for the Distance-Constrained Capacitated VRP with
Stochastic Travel and Service Times. Although no time windows are considered, the authors conclude
that PH distributions can be used to handle them. [34] focus on the VRP with time windows and
stochastic travel and service times modeled as a CCP and solved using an Iterative Local Search pro-
cedure mixed with a discrete approximation on arrival times. Travel and service times are considered
as normally distributed. Results were presented for seven Solomon [41] instances with 100 customers.
The purpose of this study is: (1) to tackle the SVRP with hard time windows, instead of soft time
windows as is frequently executed in the literature; (2) to propose a recursive approach to estimate
mean and variance of arrival times, including the eect of late arrivals at previous customers; (3) given
the fact that arrival times probability distribution are in general unknown, and in most cases expensive
to calculate, a log-normal approximation is selected among other probability distributions; and (4) to
propose a Multi-Population Memetic Algorithm (MPMA) exploiting dierent characteristics in each
population to enhance its overall performance. Moreover, the contributions found in this chapter
are: it is shown that despite the eects of hard time windows and the sum of random variables with
dierent probability distributions, the proposed approximation of the arrival times is a valid and fast
approach to guide a solution algorithm. Moreover, the experiments made with the MPMA using
dierent populations reveal that tackling the problem with dierent assumptions on the parameters,
while the populations share their solutions, greatly improves the best and average solutions found.
Furthermore, the use of a combined model (CCP + SPR) allows it to easily adapt the problem to
comprise dierent objectives, such as costs and customers satisfaction.
This chapter is organized as follows. The SVRP with hard time windows and stochastic service
and travel times is introduced in section 4.2. The approach to estimate arrival times is assessed in
section 4.3. In section 4.4, the Multi-Population Memetic Algorithm developed to solve the problem
is described. Numerical results are reported in section 4.5, and lastly a conclusion is put forward in
section 4.6.
4.2 Problem Denition
The Vehicle Routing Problem with hard Time Windows and Stochastic Travel and Service Times
(SVRPTW) is a generalization of the VRPTW where travel and service times are modeled by random
variables. The VRPTW is dened by a vertex set V = 0, 1, . . . , i, . . . , n and an edge set E =
[i, j]∀i, j ∈ V | i < j that composes of a complete graph G = (V, E). Each vertex i ∈ V is
characterized by a coordinate (xi, yi), and a time window [ei, li] in which the service must start; eibeing the opening time and li the closure time. Furthermore, Vc = V\ 0 is the customers subset,each of which has a non-negative demand qi and a specied service time si. Vertex 0 represents a
depot where a eet of homogenous vehicles with a limited capacity Q is located. In addition, to each
edge [i, j] ∈ E there is an associated non-negative cost cij and a travel time tij .
Let M be the xed cost associated to each used vehicle, and the vertex n + 1 a dummy copy
69
4.2. PROBLEM DEFINITION
of the depot 0 with the same location and time window, moreover allow V ′ = V ∪ n+ 1 and
E ′ = E ∪ [i, n+ 1] ∀i ∈ Vc with Vc = V\ 0. Service times and travel times are random variables
denoted by si(∀i ∈ Vc) and tij (∀ [i, j] ∈ E ′) respectively.To model the SVRPTW, a combined CCP and SPR formulation is used to deal with dierent
settings. On the one hand, the CCP part lets managerial decisions to be considered when solving the
problem (by controlling the condence levels). Conversely, the SPR adds the recourse cost component,
hence ensuring a more reliable measure of the quality of the solution. Our model integrates stochastic
constraints (CCP) to guarantee the condence levels α, β and γ for hard time windows constraints
at clients, depot time window, and success for the whole set of routes respectively. For simplicity, it
is assumed that every client in Vc has the same required condence level α. Let r be a route dened
as an ordered sequence of clients r = r0 = 0, r1, . . . , rj , . . . , rk, rk+1 = n+ 1 where rj represents thejth visited customer. Furthermore, AT rj stands for the arrival time to client rj ∈ Vc on the route r
and P (A) the probability of an event A. AT rj is a random variable because it depends on travel and
service times which are dened as random variables. Aiming to guarantee the condence levels to the
above-mentioned, the following constraints are imposed on every route r:
P(AT rj ≤ lrj
)≥ α ∀rj ∈ Vc (4.1)
P(AT rk+1
≤ l0)≥ β (4.2)
Constraint (4.1) enforces a service level for every customer, stating that the customer service must
start within its time window a with probability of at least α. Also by (4.2) it is guaranteed that
vehicles will return within the depot time window with a probability of at least β. Even when a route
r meets equations (4.1) and (4.2), sometimes it can miss the customer or depot time window closure;
these events are called failures. Let Ur be the probability of having no failures in a route r. A solution
for the SVRPTW will normally be composed of more than one route, therefore let s be a solution for
the SVRPTW composed by a set K of routes; the following constraint is imposed for every solution:∏r∈s
Ur ≥ γ (4.3)
Equation (4.3) guarantees a service level on the whole route plan rather than only on customers
[12], i.e. none of the routes in the solution will miss time a window constraint with customers or at the
depot, with a probability γ. This formulation is valid if, and only if, the set of routes in the solution
are independent. This assumption holds if travel times are independent and also if every client is
visited by only one vehicle, i.e. the probability of no failures is not related from one route to others.
The recourse action is considered as follows: if a vehicle arrives at a customer i ∈ Vc later than the
closure of its time window, the vehicle will continue its route (without performing the service) towards
the next client. The recourse is founded in the idea of rescheduling the visit of customer i later. The
associated recourse cost can be seen as a penalization for missing the customer time window, this
penalty is represented by the cost of a vehicle visiting exclusively customer i. The recourse albeit
simple, ts the maintenance scheduling problem. As far as service and travel times can only be known
until they are completed, technicians cannot anticipate failures. Therefore, a failure is only known at
the customer location where it takes place. Furthermore, this simple recourse lets us introduce the
cost of missing a service while keeping the computations of probabilities tractable.
The proposed recourse has already been addressed in the literature: Nguyen et al. [39] described
this recourse action, nevertheless, it is not used in their approach; Wang and Regan [48] also proposed
this recourse but the authors did not associate a cost to it. In this work we dene the expected cost
of a route r as:
cr = M +
k∑j=0
crjrj+1 +
k∑j=1
P(AT rj > lrj
)·(2 · c0,rj +M
)(4.4)
70
CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
The rst part of (4.4) stands for the xed vehicle costs M ; the second part is the cost of edges
traversed by the route and the third represents the expected recourse cost. It is important to note
that the recourse cost does not depend on the length of the delay for the late arrival as it only uses
the probability of missing the time window.
In here, the eet size is considered unlimited and homogenous. The probability density functions
ψi (∀i ∈ Vc) associated with service times are known, and service and travel times are assumed to be
mutually independent. The probability density function φij∀ [i, j] ∈ E of every travel time is known.
We set the capacity of the vehicles to innity (Q =∞) considering that it does not limit the technicians
capacity to provide their services. We assume that service times are identically Gamma distributed
and travel times are identically distributed with a Log-normal distribution. Kaparias et al. [23] and
Lecluyse et al. [29] have already used log-normal distributions, recognizing the importance of skewed
distributions to model travel times. Furthermore, Gamma distribution is selected for the service times
as long as it respects the principle of increasing repair rate.
4.3 Estimation of arrival times
4.3.1 Arrival and starting service times denition
Since the vehicles arrival times to customers and depot depends on travel and service times which are
by denition random variables, thus arrival times become random variables too. The same condition
applies to the starting time when the service is performed at a customer, as long as it depends on the
arrival times and time windows. Let ST rj denote the random starting time of the service at client
rj ∈ Vc in a route r. Because of the problems dened, the service is performed only if AT rj ≤ lrj .
Otherwise, if a failure takes place (AT rj > lrj ) the vehicle continues towards the next node in its route,
i.e. the service is not performed. Also, because time windows are hard, the service for a customer can
only start at or after the opening of its time window. Let 1Z be an indicator function which takes
value one if a condition Z holds or zero otherwise, the time when a service starts at a customer can
thus be set as:
ST rj = erj · 1AT rj<erj + AT rj · 1erj≤AT rj≤lrj (4.5)
Aiming to dene arrival times and considering the used recourse, it is mandatory to check if a failure
took place at the last customer visited or not. Therefore, arrival times can be dened by equation (4.6).
It should be noted that recursively using equation (4.6) implies that failures aect the distribution of
the arrival and initial service times.
AT rj =
ST rj−1 + srj−1 + trj−1rj , AT rj−1 ≤ lrj−1
AT rj−1 + trj−1rj , AT rj−1 > lrj−1
(4.6)
Figure 4.1 presents a graphical example of both arrival times and initial service times. The example
presents a technician arriving at customer i (a), performing its service if he arrives before li (b), or
continuing its route, and then going to node j (e). Furthermore, gure 4.1 part (a) presents vertical
lines representing ei and li at time 500 and 540. The parts (c) and (d) of the gure show the density
function of service time for customer i and the travel time from i to j, respectively. The arrival
time at node j presented in part (e) also shows the eects of time windows. Since time windows
are hard, the use of convolution properties are precluded, making it harder to properly model the
arrival times. Thus, AT rj is usually approximated by a random variable AT rj∀j ∈ V with a known
distribution allowing tractable computations. The route cost equation can then be rewritten in terms
of the approximated arrival times.
cr = M +
k∑j=0
crjrj+1 +
k∑j=1
P(AT rj > lrj
)·(2 · c0,rj +M
)(4.7)
71
4.3. ESTIMATION OF ARRIVAL TIMES
420 440 460 480 500 520 540 560
Arrival Time at i (a)
Time
Den
sity
500 510 520 530 540
Start Service Time at i (b)
Time
Den
sity
20 40 60 80 100
Maintenance time (c)
Time
Den
sity
0 20 40 60 80
Travel Time i to j (d)
Time
Den
sity
520 540 560 580 600 620 640 660
Arrival Time at j (e)
Time
Den
sity
Figure 4.1: Arrival and starting service times example.
4.3.2 Mean and Variance estimation
To estimate the mean and variance of the arrival times an approach similar to the one used by Ehmke
et al. [11] is proposed, nevertheless, we do take into account the impact of possible service failures on
AT rj as well as the fact that service times are random.
Let µX = E[X]be the expected value of a variable X and σ2
X= V ar
[X]its variance. By deni-
tion the standard deviation is set to σX =√σ2X. For simplicity let P 1 = P
(erj ≤ AT rj ≤ lrj | AT rj ≤ lrj
)and P 2 = 1 − P1 = P
(AT rj < erj | AT rj ≤ lrj
). Applying the laws of total expectation and total
variance to the equation (4.5), the mean and variance for the initial service times at customer j are
presented in equations (4.8) and (4.9).
µST rj |AT rj≤lrj
= µAT rj |erj≤AT rj≤lrj
· P 1 + erj · P 2 (4.8)
σ2ST rj |AT rj≤lrj
= σ2AT rj |erj≤AT rj≤lrj
· P 1 + e2rj · P 1 · P 2
+µ2AT rj |erj≤AT rj≤lrj
· P 1 · P 2 − 2 · erj · µAT rj |erj≤AT rj≤lrj · P 1 · P 2 (4.9)
Using the results from equations (4.8) and (4.9), we now apply the laws of total expectation and total
variance to equation (4.6). Again for simplicity let P 3 = P(AT rj−1
≤ lrj−1
)and P 4 = 1 − P 3 =
P(AT rj−1 > lrj−1
). The mean and variance for the arrival times at a node j are dened by equations
(4.10) and (4.11).
µAT rj
=(µST rj−1
|AT rj−1≤lrj−1
+ µsrj−1+ µtrj−1rj
)· P 3
+(µAT rj−1
|AT rj−1>lrj−1
+ µtrj−1rj
)· P 4 (4.10)
72
CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
σ2AT rj
=
(σ2ST rj−1
|AT rj−1≤lrj−1
+ σ2srj−1
+ σ2trj−1rj
)· P 3
+
(σ2AT rj−1
|AT rj−1>lrj−1
+ σ2trj−1rj
)· P 4
+ (P 3 · P 4) ·(µST rj−1
|AT rj−1≤lrj−1
+ µsrj−1− µ
AT rj−1|AT rj−1
>lrj−1
)2(4.11)
Although the equations (4.8) to (4.11) are computed for AT rj , they are valid for AT rj too, since no
assumption is made on the distribution. Moreover, by using AT rj instead of AT rj two problems are
solved. The rst one is that the calculus of probabilities can be easily made. The second is that the
calculation of the mean and variance of the truncated variables, e.g. the mean of the upper trun-
cated arrival time (µAT rj |AT rj≤lrj
), can be evaluated, for example using the closed forms expressions
gathered in [21] for several distributions.
Summing up, by iteratively applying equations (4.8) to (4.11) using AT rj , one can calculate the
parameters of the AT rj distribution for a given route. Assuming a technician starts its route from
the depot at time zero with no variance, and using equations (4.8) and (4.9), the mean and variance
of the starting service time at the rst customer are derived. This can be easily achieved since AT r1depends only on the travel time from the depot to r1. Next, the parameters of the arrival time at the
second customer can be calculated using equations (4.10) and (4.11), all the more, after performing
this step, equations (4.8) and (4.9) are used to compute the parameters of the starting service time
at the second customer. The process is repeated until the whole route is evaluated. Additionally, by
replacing AT rj in constraints (4.1) and (4.2), the feasibility of the route can be checked.
Multiple authors have used dierent distributions and assumptions to estimate the arrival times
(readers can refer to [7, 11, 18, 34]). It is assumed that AT rj follows a log-normal probability dis-
tribution. The log-normal assumption is twofold motivated. First, an experiment using Monte Carlo
simulation shows that statistically, the log-normal distribution tted the arrival times better than
other distributions (e.g. normal, Gamma). Second, although normality assumption has been proven
to be eective in previous works [7, 11]; skewness is an important factor to be considered in stochastic
vehicle routing algorithms to lead to reliable routing decisions [18]. The presence of left truncation
due to early arrivals induces more asymmetry in the time when service starts which is transferred to
arrival times, and because normal distribution has zero-skewness it does not appear to be the best
distribution to approximate arrival times. Based on these reasons and the tractable computational
times, the log-normal assumption is retained.
4.3.3 Validating the log-normality approximation
An experiment to validate the log-normality assumption is conducted as follows. A solution is rst
created for each instance (see section 4.5.1 for further detail on the instances) using the Clarke and
Wright [9] heuristic combined with the simulation procedure to verify constraints (4.1, and 4.2). For
each tested instance, we considered the two routes in the solution with the greatest number of visited
clients. The reason for this choice is that larger routes will be harder to t and to approximate as
far as more truncation eects are summed up. Furthermore, for the whole set of selected routes a
test is performed to compare the mean, standard deviation, and percentiles of the arrival times at
each node. This is estimated through simulation (10000 trials) against the values obtained while con-
sidering the variables AT i log-normally distributed (estimated). The reported gaps are calculated as|Xsimulation−Xestimated|
Xsimulationwhere Xsimulation and Xestimated are replaced by the mean, standard deviation
and each percentile of the arrival times. Table 4.1 shows basic information of the experiments, includ-
ing the number of evaluated routes, the total number of evaluated nodes, the average number of nodes
per route and the percentage of arrival times that tted log-normal, gamma and normal distributions.
Although more classic probability distributions were tested e.g. exponential, Weibull, chi-squared,
Poisson, Pareto, triangular, uniform, Cauchy, logistic, Laplace, and Erlang, we reported the results
73
4.3. ESTIMATION OF ARRIVAL TIMES
Table 4.1: Basic experiment information per family of instances
InstanceFamily
Num.routes
Totalnodes
Avg.customersper route
% Fittedlog-normal
% FittedGamma
% FittedNormal
C1 18 195 9.38 35.03% 36.16% 15.82%C2 16 460 27.75 23.42% 21.17% 10.81%R1 24 219 8.13 46.67% 40.00% 18.97%R2 22 363 15.5 40.47% 34.31% 23.17%RC1 16 143 7.94 28.35% 18.11% 9.45%RC2 16 241 14.06 30.67% 25.33% 16.44%
Table 4.2: Arrival times average absolute gaps between simulated values and log-normal approximationNotable percentiles 1st to 99th percentiles
InstanceFamily
Mean SD 90% 95% 99% Mean Min. Max.
C1 0.10% 1.41% 0.21% 0.31% 0.70% 0.26% 0.15% 0.97%C2 0.06% 2.92% 0.19% 0.36% 0.78% 0.21% 0.10% 0.78%R1 0.07% 0.76% 0.13% 0.28% 0.73% 0.20% 0.11% 0.73%R2 0.03% 0.73% 0.06% 0.12% 0.29% 0.08% 0.04% 0.29%RC1 0.08% 1.57% 0.25% 0.58% 1.45% 0.44% 0.17% 1.74%RC2 0.03% 0.77% 0.08% 0.17% 0.41% 0.11% 0.04% 0.42%
only for the best three distributions. A total of 112 routes were used in the analysis accounting for a
total of 1621 evaluated nodes. The number of routes diers from family to family since the number
of instances in each family is dierent (except for C2, RC1, and RC2 with 8 instances). Instances of
type 2 have a larger number of clients per route as expected because they have larger time windows.
Table 4.1 presents the percentage of nodes that tted log-normal, Gamma and normal distributions
respectively. The standard Kolmogorov-Smirnov test was used for testing the distributions. The rst
customer of each route was not considered because the arrival time distribution will be the same dis-
tribution of travel times between the depot and the customer. The test concludes that nearly one in
three of the arrival times are log-normal distributed while this number decrease to around 15% when
tting a normal distribution. Gamma distribution achieves to t around 28% of arrival times. Based
on these results it appears that the log-normal approximation can be used as a compromise solution
to model the arrival times while allowing tractable computation of the cost function and probabilities.
Thus, we selected it to model AT rj and continued testing its pertinence. As shown in Table 4.2, the
proposed approximation displays an average gap of the arrival times mean of at most one tenth of
one percent, showing good performance. The lowest average gap is reached by families R2 and RC2
where the mean has an average gap of only 0.03% while the highest gap of 0.1% is achieved in family
C1. On this, standard deviations (SD) have bigger gaps, yet, half of the studied families present an
average standard deviation gap below one percent, and only one family of instances exceeds a gap of
two percent.
Given that equations (4.1) and (4.2) make use of probabilities, it is mandatory to test how accurate
the estimations of dierent percentiles are. Therefore, table 4.2 also presents the average absolute gap
for some notable percentiles and the mean, minimum, and maximum, average absolute gap when the
1st to 99th percentiles are evaluated with increments of one percent.
Results in table 4.2 show the pertinence of the log-normality assumption. The 90th percentile has
an average gap under a quarter of one percent, while the 95th percentile estimation is on average
quite bellow one percent error rate for the whole set of instances. Moreover, the 99th percentile
gives an average gap less than one percent for all families except for RC1 for which the maximum is
reached at 1.45%. It should be noted that the errors grow with the percentile value. Table 4.2 also
reects the accuracy of the estimations, with a mean gap across the families and from the 1st to 99th
percentile inferior to half a percent. Again, RC1 shows the highest gaps in terms of mean, minimum
and maximum average gap when compared with the rest of the families. Type 2 instances (C2, R2,
RC2) look to have lower gaps than their counterparts (C1, R1, RC1) which might be explained by
74
CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
0,00%
1,00%
2,00%
3,00%
4,00%
5,00%
6,00%
0,00% 5,00% 10,00% 15,00% 20,00% 25,00% 30,00%
Mea
n a
bso
lute
err
or
per
cen
tile
95
% of noise Travel Times
30,00%
25,00%
20,00%
15,00%
10,00%
5,00%
0,00%
Figure 4.2: Average absolute error of 95% percentile with service and travel time noises.
the fact that larger time windows prevent the truncation of the arrival times.
Table 4.3: Arrival times mean and standard deviation absolute gaps between simulated values andthree approximations
Log-Normal Normal GammaInstanceFamily
Mean SD Mean SD Mean SD
C1 0.10% 1.41% 0.22% 2.96% 0.22% 3.09%C2 0.06% 2.92% 0.15% 4.79% 0.15% 4.51%R1 0.07% 0.76% 0.11% 1.99% 0.11% 1.80%R2 0.03% 0.73% 0.03% 0.90% 0.03% 0.84%RC1 0.08% 1.57% 0.12% 3.11% 0.11% 2.40%RC2 0.03% 0.77% 0.03% 1.21% 0.03% 1.06%
The proposed approach is very exible and can easily modify the assumption that AT rj is log-
normally distributed to other probability distributions, e.g. Normal, Gamma, Exponential. In fact,
the mean and standard deviation of AT rj were also calculated assuming AT rj is normally and Gamma
distributed. However, log-normal approach presents the best results, improving by more than 80%
of the absolute gap error of arrival times' mean when compared to the normality assumption (see
table 4.3). Indeed, these results with those reported in table 4.2 show that the log-normal distribution
gives better results in terms of parameters and percentiles estimations. Therefore, we retained this
distribution as the best option. To check the robustness of the log-normal approximation we conducted
an additional experiment. During the simulation of the routes, we added white noise to the realization
of the travel and service times. The aim was to inspect if the results held up under these scenarios,
thus representing the uncertainties in the probability distributions of both the travel and service times.
Noise was added using a normal random variables X with parameters µX = 0 and standard deviations
as a percentage of the expected value in travel and service times. This percentage varied from 0 to
0.3 with steps of 0.05. Figure 4.2 shows the average absolute gap error of percentile 95% for dierent
levels of noise in travel and service times. Percentile 95% is selected since it is commonly used as a
standard service level. The results show that the maximum absolute error is just above 5% when noise
of travel and service times is 30% of their means. However, if noise levels are at 15% or less, the error
is below 2%. Indeed, even at noise values of 20% the error stays under the 3% mark. Therefore, even
with the presence of noise in times, the log-normal approximation reaches small gaps at percentiles
estimation.
4.4 Multi-population Memetic Algorithm
To solve the SVRPTWwe propose to use a Multi-Population Memetic Algorithm (MPMA) framework.
The choice of a MA is due to its exibility and performance in a variety of VRPs (for example see
[40, 47]). For the sake of clarity this section is structured as follows: rst the MA general structure
75
4.4. MULTI-POPULATION MEMETIC ALGORITHM
is presented, this is followed by details of the main components of the proposed MA. Finally we will
present the Multi-Population MA general framework.
4.4.1 MA general structure
A Memetic Algorithm (MA) can be described as the hybridization of a Genetic Algorithm with local
search procedures [36]. Our MA structure follows the ideas of Prins [40] and is summarized in algorithm
3. The MA starts by creating a population (line 1 of Algorithm 3), which is a set of encoded solutions
to the problem at hand. The MA iterates until a stopping criteria is met (line 12 Algorithm 3). At
each iteration a new chromosome (C) is processed. The chromosome C can suer a mutation process
(line 4 Algorithm 3) or it can be decoded to perform a local search (lines 5 and 6 of Algorithm 3).
Then at line 11 of Algorithm 3 the population is updated considering C. Each of the components of
the MA and its procedures is now explained in detail.
Algorithm 3 MA1: Pop← Generate initial population2: repeat
3: C ← Crossover(Pop)4: C ← Mutate(C) with probability pm.5: D ← Decode(C).
6: D′← LocalSearch(D) with probability pls.
7: if Costs(D′) < Costs(D) then
8: C ← Encode(D′)
9: D ← Decode(C).10: end if
11: Process(C,D,Pop)12: until Stopping criteria
4.4.2 Chromosomes
Encoding
A chromosome is an encoded solution to the tackled problem. The MA chromosomes that we use are
coded as permutations of the n clients. The decoded version of a chromosome is composed of a set
of routes that satises the problem constraints. Henceforth we use the term chromosome to make
reference to the permutation representation while the term individual is used to dene its associated
decoded solution.
Decoding
To decode a chromosome, we use the Split method of Prins [40]. Split works by considering an auxiliary
directed graph H = (W,Y ) with vertex set W = W 0 = 0,W 1, · · · ,W i, · · · ,Wn. W 0 represents a
dummy vertex and verticesW 1 . . .Wn ∈ Vc characterizes an ordered sequence of customers dened bya chromosome. An arc (W i,W i+d) represents a feasible route for visiting the customers fromW i+1 to
W i+d, with its associated cost. The Split procedure nds the shortest path from vertex W 0 to vertex
Wn and subsequently the optimal set of routes associated to the chromosome. Normally this can be
done using a shortest path algorithm. Nevertheless, constraint (4.3) precludes this implementation
for the SVRPTW. As far as constraint (4.3), it is dependent on the set of routes which comprises a
solution, this constraint must be guaranteed when decoding a chromosome.
∑r∈K
ζr ≤ θ (4.12)
To overcome this, we rewrite constraint (4.3) as done by Errico et al. [12]. Note that setting
ζr = − ln (Ur) and θ = − ln (γ), constraint (4.3) is equivalent to equation (4.12). By considering
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CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
0
2
4
1
5
3
2 4 1 5 3
0 1 52 4 3
𝜇 ሚ𝑡02: 25
Customers permutation (Figure 2a)
Ƹ𝑐𝑟=75.54𝑈𝑟 = 0.97
0
2
4
1
5
3
Split auxiliary graph (Figure 2c)
Decoded routes from permutation (Figure 2d)Times information (Figure 2b)
[25,40][𝑒𝑖 , 𝑙𝑖] [40,90] [60,95] [30,60] [35,85]
𝜇 ሚ𝑡04: 30
𝜇 ሚ𝑡01: 20
𝜇 ሚ𝑡05: 15
𝜇 ሚ𝑡03: 25
𝜇 ሚ𝑡24: 20
𝜇 ሚ𝑡41: 20𝜇 ሚ𝑡15: 20
𝜇 ሚ𝑡53: 20
𝜇 ǁ𝑠2: 20
𝜇 ǁ𝑠2: 20
𝜇 ǁ𝑠2: 20
𝜇 ǁ𝑠2: 20
𝜇 ǁ𝑠2: 20
Ƹ𝑐𝑟=71.78𝑈𝑟 = 0.93
Ƹ𝑐𝑟=60.75𝑈𝑟 = 0.98
Ƹ𝑐𝑟=50.34𝑈𝑟 = 0.99
Ƹ𝑐𝑟= 60𝑈𝑟 = 1
Ƹ𝑐𝑟= 40𝑈𝑟 = 1
Ƹ𝑐𝑟= 30𝑈𝑟 = 1
Ƹ𝑐𝑟= 50𝑈𝑟 = 1
Chromosome cost: 75.54+40+60.75 = 176.29Probability no failures: 0.97x1x0.98 = 95.06%
Figure 4.3: Split example for the VRP with stochastic travel and service times
equation (4.12), the shortest path problem involved in the Split comes back to solve a Constrained
Shortest Path Problem (CSPP). In this CSPP the scarce resource is θ and each edge consumes ζr units
of the resource, where r is the route associated with the edge. The CSPP is solved using a Labeling
Algorithm as the one employed in [14]. Thus, Split allows to decode the optimal routes partition for
a chromosome, giving a solution which is feasible, i.e. respect constraints (4.1) to (4.3).
An example of the split method using the log-normal approximation is reported in gure 4.3. For
this example it is assumed that α, β, γ are set to 95%. Customer time windows are reported in part
(a) of the gure along with the chromosome. Moreover, a coecient of variation of 0.2 is used for the
travel and service times. Their mean values are reported in part (b). Distances are assumed to be
equal to the mean of travel times. For simplicity, the xed cost of each vehicle is assumed to be zero.
Besides, the time windows for the depot are dened by e0 = 0 and l0 = 140. Part (c) of gure 4.3
shows the auxiliary graph of the Split, and the bold arcs represent the optimal solution to the CSPP.
In this auxiliary graph the arc (2, 1) for example, represents a route starting at the depot, visiting
customers 4 and 1, and then returning to the depot. This route has an expected cost (see equation
4.7) of 71.8 and a probability of 93% of having no failures. The reader shall notice that only the arcs
representing feasible routes (respecting the service levels α and β) are considered in gure 4.3 part
(c). Lastly, part (d) displays the individual, or the decoded routes with the cost and the probability
of having no failures in the solution.
4.4.3 Population
The population (Pop) is dened as an ensemble of chromosomes, thus, an ensemble of coded solutions.
Pop is composed of PopSize chromosomes which are ordered in a decreasing way with respect to the
cost of their detailed corresponding solutions. PopSize is constant, so there are always the same
number of chromosomes in Pop. The diversity of the population is controlled by mean of Campos
et al. [6] distance measure. The latter is computed at the detailed solution level. To enhance
diversication, clones (distance zero) are discarded, and chromosomes with a positive distance are
allowed to enter. It shall be noted that the although this makes the MA a version with population
control, the distance measure is only used to avoid clones. Further improvements can be performed
by dynamically adjusting the minimum distance to accept the chromosomes willing to enter the
population1. Furthermore, as populations are of a xed size, a random chromosome among the worst
half of the population is deleted before the new one is inserted.
The initial population is created as follows: Four individuals are computed by using heuristics.
1I thank professor Christian Prins for pointing this out during the dissertation questions and comments.
77
4.4. MULTI-POPULATION MEMETIC ALGORITHM
1 6 4 2 3 7 5 8Parent 1 (𝜋1)
Parent 2 (𝜋2)New individual
2 1 5 6 4 8 3 7
5 6 4 2 3 8 7 1
i j
Figure 4.4: Example of OX crossover.
The rst is created with the Clarke and Wright[9] heuristic, the second and third from Solomon [41]
insertion heuristic (using two sets of parameters), and the fourth from Algorithm D proposed by
Nagata and Bräysy [37] using as a starting solution the best individual among the Clarke and Wright
and Solomon heuristics. All heuristics are run for the deterministic problem using the mean travel
and service times as the true values. The four heuristic solutions are converted to chromosomes by
concatenating their routes and then added to Pop. Meanwhile, to achieve the value PopSize, the
population is lled with chromosomes created with random permutations of the customers. If clones
are built during this procedure they are discarded.
4.4.4 Crossover
Crossover procedure is used to create new chromosomes from those already present in Pop. The
crossover used in this work is the well-known Ordered Crossover (OX). OX works as follows: two
chromosomes π1, π2 are selected from the population and two random positions i, j | i < j ≤ n are
chosen. The information between positions i, j is copied from π1 to the new ospring. To complete
the latter, π2 is circularly traversed from position j + 1 to position j. The customers which are not
already with the new child are then copied circularly from position j + 1 to i. The roles of π1, π2 are
exchanged to produce another ospring. Before performing the crossover, the MA selects π1, π2 with
binary tournaments, and then one of the two children generated by OX is randomly picked.
4.4.5 Local search and mutation
The local search (LS) is designed to improve the objective function of a solution by performing dierent
modications on the solution itself. The proposed local search is based on the Variable Neighborhood
Search (VNS) [35]. In this context Neighborhoods are structured in such a way that movements can
be performed on a solution s to achieve a new solution s′. Further explanation on neighborhoods
denitions for VRPs can be found in Labadie et al. [25] and Bräysy et al. [5]. The neighborhoods
used for the local search are: Or-opt, 2-opt movements, both intra and inter routes, and the CROSS
exchanges in their inter-routes version.
LS starts by searching for a movement in the rst neighborhood which improves the solution. If
such movement does not exist, it passes to the next neighborhood, however, if a new best solution
is found in the current neighborhood, the LS starts again from the rst neighborhood. The process
is repeated until no neighborhood can improve the solution. The order in which neighborhoods are
explored is randomized and it changes every time the LS is performed. A movement is executed
immediately if it improves the solution, therefore a rst accept criterion is used.
Since LS can be expensive in terms of running time, the neighborhoods exploration is constrained,
i.e. Or-opt movements are limited to movements involving, at most, three customers, and CROSS
exchanges use sequences to at most two clients. Also, LS is performed to an individual with probability
pls. Mutations are performed by selecting a random position among the chromosome, then the selected
customer and the following are relocated to another random position. Mutations are executed with a
probability of pm.
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CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
4.4.6 MPMA framework
Besides the exibility and performance of MAs they also allow a multi-population approach, e.g.
using distributed evolutionary algorithms. In the latter, several populations can be used to enhance
the diversication [1]. Our MPMA aims to enhance the diversication but also to speed-up and
improve the solutions through the introduction of dierences between the problems tackled in each
MA.
Algorithm 4 presents the framework of the proposed MPMA, it uses a set of MAs with the same
presented structure, but with dierences in the problem handled by each one. In the rst line of
Algorithm 4 the MAs are created. Then, each MA starts to solve its problem (in parallel) in lines
three to six of Algorithm 4. While the MAs are working they cooperate by letting chromosomes be
copied from one MA to another (line 8 of Algorithm 4). The aim of communication is to transfer
valuable information from one MA to another and to enhance diversication.
Once all the MAs nish, the MPMA continue its procedure. Since the MAs solutions are created
through the approximation of arrival times (see section 4.4.7), they may not be feasible in the un-
certain environment. Therefore, to guarantee a feasible solution we proceed as follows. The best ve
chromosomes of each MA are extracted (lines 10 to 12 of Algorithm 4) and decoded into individuals
using Split (lines 14 to 17 of Algorithm 4). Monte Carlo simulation is used to check the feasibility of
the routes, the probability of having no failures, and to estimate their costs.
Algorithm 4 MPMARequire: α, β, γ, τ, numSimulations, runT ime, F1: Create(MAf ) ∀f = 1...F2: initialT ime← CurrentTime3: In parallel : // The MAs start and run in parallel4: for all f = 1...F do
5: run(MAf (α, β, γ, τ, runT ime)) // See algorithm 36: end for
7: while CurrentTime - initialT ime ≤ runT ime do8: Communicate MAf ∀f = 1...F9: end while
10: for all f = 1...F do
11: Best← Best ∪Get best Chromosomes(MAf )12: end for
13: Pool← 14: for all C ∈ Best do15: Split(C,α, β, γ, numSimulations) // Using Monte Carlo Simulation16: Pool← Pool ∪ Simulated routes during Split17: end for
18: return SolveSetPartitioning(Pool, γ)
Moreover, since Split using simulation is a highly time consuming task, we take advantage of every
route simulated during the Split of the best MA chromosomes. This is done by saving each route into
a pool, which acts as a list of feasible routes that have been validated by Monte Carlo Simulation.
After all selected chromosomes have been evaluated, the routes in the pool are used to solve a set
partitioning problem (see for example [33]) which uses equation (4.12) to guarantee the feasibility of
the solution. By solving the set partitioning using the list of Monte Carlo Simulation validated routes,
we guarantee that the routes in the solution respect the problem constraints. The solution retrieved
by the set partitioning problem is returned as the best solution found by the MPMA.
4.4.7 MA dierences
To allow the communication of the MAs some changes are performed to the base MA. Every τ seconds,
each MA sends two copies of its own chromosomes. One is randomly selected from the best half of
Pop, and the other from the worst. The destination MA of each child is randomly picked. Moreover,
79
4.5. NUMERICAL RESULTS
destination MAs use the received chromosome in the next iteration of algorithm 3 instead of creating
a new one by a crossover procedure.
Our MPMA uses three MAs (K = 3), namely MA1, MA2, MA3 with the same general structure
presented in section 4.4.1 but with its own particularities. MA1 tackles the SVRPTW by embed-
ding the log-normal modeling presented in section 4.3 while MA2 and MA3 work on a deterministic
VRPTW. The idea behind MA2 and MA3 is that even if deterministic solutions might not perform
well on stochastic environments, they may serve as good seeds to potential good stochastic solutions
([3]).
MA2 and MA3 dier since MA2 travel and service times are set to their respective mean values,
whereas MA3 service and travel times are set to the 75th percentile that is si = ψi−1 (75%) ∀i ∈ Vc and
tij = φij−1 (75%) ∀ [i, j] ∈ E where ψi−1∀i ∈ Vc and φij−1∀ [i, j] ∈ E represent the inverse probability
functions of the service and travel times respectively. The 75th percentile is selected after performing
some preliminary tests. Increasing the service and travel times provides solutions with low probability
of failures. A similar idea has already been used in the context of the VRP with stochastic demands
([32]). Therefore, MA3 is used to provide robust solutions which can be fast improved upon to perform
better in a stochastic environment. Moreover, MA2 and MA3 use labels to enhance the LS procedure
(see [47]) and allow unfeasible solutions with the returns in time (time wraps as explained in [38]).
Additionally, MA2 and MA3 use the Bellman algorithm during the Split procedure (section 4.4.3) as
done by Prins [40].
MA1 presents its own important considerations. Log-normal modeling allows MA1 to check feas-
ibility, estimate the costs, and the underlying probabilities when using Split (section 4.4.3) and while
performing the LS. This increases the complexity of the LS since feasibility checks require the evalu-
ation of the whole route. Therefore, MA1 uses an additional strategy. Indeed, MA1 alternates its plseach % seconds by changing the probability of LS from zero to a given pls
′and from pls
′to zero value.
When MA1 pls is greater than zero, it uses deterministic labels to eciently evaluate the pertinence
of a movement (see [47]). The idea is that if a movement is unfeasible in a deterministic environment
(times equal to their mean values), it is very likely that it will violate constraint (4.1) or (4.2). Sub-
sequently it will be a waste of time to reevaluate the route using the log-normal approximation. If the
movement is feasible for the deterministic model, then the log-normal approach is used to evaluate its
feasibility and pertinence.
4.5 Numerical Results
4.5.1 Instances
To test the MPMA, results are gathered for modied Solomon [41] instances. Customer service times
are set as Gamma distributed variables with mean equal to the deterministic service time given in
the original instances, and a standard deviation inferred for a coecient of variation of 0.2. Edges
travel times are set as log-normally distributed variables with mean equal to the length of the edge
and with standard deviation derived from a coecient of variation of 0.2. This value is used when
considering authors like Turner et al. [46] (as cited in [11]) previously stated that it varies from 0.15
to 0.25 when dealing with freeways and from 0.20 to 0.25 for principal and secondary roads. As the
original instances do not state the kind of roads represented by the edges, the value 0.2 works overall
for any type of road. Solomon [41] proposed 56 les divided among six families, C1, R1, RC1, C2, R2,
RC2 for the VRPTW. Families C1, R1, and RC1 have tight time windows, so routes in these instances
tend to have less number of customers. On the other hand C2, R2, and RC2 have larger time windows
therefore more clients are visited by each vehicle. Another classication can be done based on the
position of customers in the space. Families C1 and C2 have clustered sets of customers, while they
are randomly positioned in R1 and R2. RC1 and RC2 have a mix of clustered and randomly located
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CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
clients. All 56 instances are comprised of a depot and 100 customers.
While considering that Solomon [41] instances are designed for the VRPTW, we performed some
modications to adapt them to deal with the stochastic nature of the SVRPTW. The service levels
α, β, γ are set to 95%, 95%, and 90% respectively. To guarantee a feasible solution, the elementary
routes (routes visiting only one customer) dened as r = r0 = 0, r1 = i, r2 = n+ 1 ∀i ∈ V c mustat least respect constraints (4.1) and (4.2). To guarantee the feasibility of these routes, each one was
tested through one million trials by simulation. If a given route r is unfeasible because of constraint
(4.1), the customer time window closure li is set to li = li + 5. If infeasibility arises because of
constraint (4.2) then, the depot time window closure is set to ln+1 = ln+1 + 5. This process is
repeated until feasibility is reached for every route visiting a single customer.
4.5.2 General discussion
The MPMA presented in section 4.4 was tested on the modied instances of Solomong [41] introduced
in section 4.5.1. We used the instances including the rst 50 customers and the complete instances.
MPMA was implemented on a Dell Latitude E6420 personal computer with Intel Core i7-2760QM
@2.4 GHz, running Windows 7 Professional 64 bits. The algorithms were coded in Java and compiled
with JavaSE-1.8_45, with maximum allocated memory of 1 Gb. Random variables and computation
probabilities were generated by the library of Stochastic Simulation in Java ([30]). Parameter values
were selected after several preliminary tests. The vehicle cost M is xed to 1000, this value allows the
MPMA to minimize the number of vehicles as the rst objective followed by the total distance plus
recourse cost. MA populations size PopSize is set to 25 for the three MAs. When simulation is used
during the MPMA it performs 1000 trials to evaluate the feasibility, estimate the cost and required
probabilities for each route. The MPMA performs 10 runs for the whole set of instances (50 and 100
customers). % is set to n/10 seconds, while τ value is 0.05 seconds. The pls and pm are set to 0.2
for all MAs although MA1 starts without performing LS. The MAs stopping condition is set to 20
seconds for the 50 customer instances while this value is increased to 100 seconds for the instances
with 100 customers. Set partitioning problem is solved by means of Gurobi 6.0 ([20]).
Tables 4.4 to 4.6 presents the best solutions found out of the 10 runs for C, R, and RC instances
respectively. Meanwhile, average results can be found at tables 4.11 and 4.12. For each instance the
expected route cost (ERC), the number of vehicles (# Veh.), the distance (DC), and the recourse cost
(RC) are reported. Type 1 instances show higher recourse costs than type 2 instances. This is quietly
pronounced in family R (100 customers) since the average recourse cost is increased from R2 to R1
by 605%; similarly families C and RC display the same behavior. This is completely coherent since
type 1 instances have tighter time windows which imply that failures are more likely to occur, and
consequently recourse costs are higher.
Furthermore, the family C instance shows an increment of nearly 106% in the cumulative number
of vehicles (CNV) required for 100 customer instances when compared to 50 customer instances. The
rise of the total costs is near to 105%, while distance and recourse increases by 96.9% and 83.1%
respectively. Family R on the other hand, shows the minimum increments (among C, R, and RC
families), with an augmentation of nearly 72.8% in the CNV, 70.3% in terms of cost and 50.8% and
71.1% for distance and recourse metrics. Moreover, RC family raises the CNV by 79.5% and the total
cost by 78.1%. The distance is increased by nearly 69.2%, and recourse presents the lowest increment
of only 26.1%. Also, it should be noted that the best CNV for 50 customer instances is 302 while
the same metric for 100 customer instances is 554. This dierence of 252 vehicles is translated into
an increase of 83.4%. A similar behavior was observed for the deterministic best solutions found by
MA2, which incremented the CNV by a factor of three quarters when passing from 50 customer to
100 customer instances. Thus, the increments in the CNV seem to be driven mostly by the inherent
combinatorial nature of the problems whether the stochastic or deterministic versions are considered.
Table 4.7 summarizes the results attained by MPMA. For each family of instances they are reported
81
4.5. NUMERICAL RESULTS
Table 4.4: Best solutions found by MPMA for C type instancesBest Solution - 50 Customers Best Solution - 100 Customers
Instance ERC # Veh. DC RC ERC # Veh. DC RC
C101 9741.22 9.00 642.90 98.32 21519.83 20.00 1497.54 22.29C102 8635.27 8.00 590.81 44.46 18391.69 17.00 1330.67 61.02C103 7534.46 7.00 514.58 19.88 15227.97 14.00 1193.63 34.33C104 5382.54 5.00 369.45 13.10 11986.00 11.00 961.25 24.75C105 7555.61 7.00 522.88 32.73 16292.06 15.00 1199.72 92.35C106 8609.68 8.00 564.34 45.34 16259.28 15.00 1212.13 47.15C107 6506.86 6.00 484.18 22.68 14171.11 13.00 1095.82 75.29C108 6467.17 6.00 440.31 26.86 13069.71 12.00 956.64 113.07C109 5369.22 5.00 364.11 5.11 10846.15 10.00 836.91 9.24C201 3432.76 3.00 417.42 15.34 5821.75 5.00 717.08 104.67C202 2462.64 2.00 433.43 29.20 5755.96 5.00 718.78 37.18C203 2514.33 2.00 501.01 13.32 4715.41 4.00 679.56 35.85C204 2384.27 2.00 372.21 12.06 4667.72 4.00 654.73 12.99C205 2464.49 2.00 460.92 3.57 4688.49 4.00 671.74 16.75C206 2460.33 2.00 456.51 3.82 4666.89 4.00 656.51 10.38C207 2449.09 2.00 448.27 0.82 4655.02 4.00 649.24 5.78C208 2366.15 2.00 364.61 1.54 4632.91 4.00 625.22 7.69
Total 86336.10 78.00 7947.94 388.16 177367.9 161.00 15657.17 710.78
Table 4.5: Best solutions found by MPMA for R type instancesBest Solution - 50 Customers Best Solution - 100 Customers
Instance ERC # Veh. DC RC ERC # Veh. DC RC
R101 16232.47 15.00 1191.51 40.96 28960.67 27.00 1934.28 26.38R102 14082.91 13.00 1049.13 33.78 25744.05 24.00 1725.49 18.56R103 10890.01 10.00 867.52 22.49 19409.84 18.00 1371.98 37.86R104 7734.53 7.00 722.66 11.88 14154.45 13.00 1128.20 26.25R105 12007.66 11.00 986.34 21.32 19602.65 18.00 1527.25 75.40R106 9898.11 9.00 867.16 30.95 17420.11 16.00 1369.40 50.71R107 8795.05 8.00 775.87 19.18 14258.45 13.00 1194.59 63.86R108 6659.90 6.00 631.58 28.32 12068.34 11.00 1040.52 27.82R109 9863.25 9.00 849.35 13.90 15308.71 14.00 1270.43 38.28R110 8779.34 8.00 762.32 17.02 14239.67 13.00 1193.42 46.25R111 8751.49 8.00 745.91 5.58 14184.60 13.00 1151.82 32.78R112 7678.27 7.00 672.68 5.58 13035.89 12.00 1029.60 6.29R201 3898.66 3.00 892.30 6.36 6333.61 5.00 1306.96 26.65R202 3745.80 3.00 739.81 5.99 6090.63 5.00 1079.22 11.41R203 2726.72 2.00 703.38 23.34 4927.94 4.00 916.71 11.23R204 2512.02 2.00 511.28 0.74 3792.57 3.00 792.53 0.03R205 2774.60 2.00 768.96 5.64 6011.41 5.00 1011.05 0.36R206 3642.05 3.00 642.02 0.04 3981.86 3.00 981.50 0.35R207 2587.16 2.00 587.16 0.00 3835.66 3.00 835.66 0.00R208 2493.69 2.00 493.69 0.00 3715.32 3.00 710.28 5.04R209 2694.99 2.00 693.19 1.80 4926.19 4.00 918.85 7.34R210 2697.69 2.00 692.05 5.63 4993.53 4.00 992.32 1.21R211 2561.32 2.00 561.28 0.03 4778.41 4.00 778.20 0.20
Total 153707.6 136.00 17407.14 300.54 261774.5 235.00 26260.27 514.25
Table 4.6: Best solutions found by MPMA for RC type instancesBest Solution - 50 Customers Best Solution - 100 Customers
Instance ERC # Veh. DC RC ERC # Veh. DC RC
RC101 12133.54 11.00 1091.58 41.96 20920.01 19.00 1819.27 100.73RC102 9947.87 9.00 901.19 46.68 18714.96 17.00 1660.41 54.55RC103 8883.27 8.00 848.98 34.29 15475.45 14.00 1427.97 47.48RC104 6797.88 6.00 728.56 69.32 13300.61 12.00 1260.09 40.51RC105 10989.11 10.00 969.13 19.98 18783.43 17.00 1704.47 78.96RC106 8935.65 8.00 904.74 30.91 16627.30 15.00 1597.60 29.70RC107 7827.67 7.00 803.86 23.80 15433.93 14.00 1387.96 45.97RC108 6738.07 6.00 710.82 27.25 14321.21 13.00 1309.87 11.34RC201 4825.24 4.00 820.78 4.46 7402.04 6.00 1387.02 15.02RC202 3836.37 3.00 830.57 5.80 6184.45 5.00 1181.73 2.72RC203 3640.20 3.00 633.32 6.88 5098.22 4.00 1096.44 1.78RC204 2498.75 2.00 490.95 7.80 4801.08 4.00 800.55 0.53RC205 4684.67 4.00 676.94 7.73 7245.54 6.00 1229.47 16.07RC206 2914.94 2.00 856.71 58.23 5229.15 4.00 1195.54 33.60RC207 3617.36 3.00 614.62 2.74 5071.24 4.00 1067.12 4.11RC208 2531.56 2.00 525.87 5.68 4879.98 4.00 866.94 13.04
Total 100802.1 88.00 12408.62 393.52 179488.5 158.00 20992.47 496.11
Table 4.7: Average performance of MPMA50 Customer instances 100 Customer instances
InstanceFamily
Avg. #Veh.
% TimesBSF
Avg.Time(s)
Avg. #Veh.
% TimesBSF
Avg.Time(s)
C1 7.04 52.3% 37.61 14.33 57.7% 132.14C2 2.38 57.5% 61.00 4.28 70.0% 188.82R1 9.36 65.0% 35.14 16.48 29.2% 128.05R2 2.63 26.4% 57.94 4.51 14.54% 186.06RC1 8.31 61.25% 36.18 15.54 38.8% 127.65RC2 3.24 32.50% 51.49 5.29 11.25% 170.59
82
CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
as follows: the average number of vehicles (Avg. # Veh.), the percentage of times the best solution is
found (% Times BSF), and the average from 10 runs. The average time stands for the time expended
by MPMA in one run of one instance. It comprises from the start time of the algorithm until the
best solution is retrieved, i.e. it does not take into account the total computational time generated
by parallel threads. To establish if two solutions are equal, we simulate the best solution found at
each run of the MPMA, which allows us to construct a condence interval of 95% on the total costs2.
If two condence interval solutions overlap, it is assumed that they are, on average, equal. Detailed
results of the best an average solutions are presented in tables 4.11 to 4.12.
Type 2 instances with random located customers are harder to solve than type 1 instances when
considering the percentage of times that the best solution is found. This is evident for family RC2
with 100 customers, where the best solution is only found 11.25% of the time among the 10 runs.
Still, 4 out of 6 families found the best solution almost more than 30% of the times in 100 customer
instances. In contrast, C2 family (with 100 customers) achieves 70% of the time for the best solution.
Overall the MPMA shows a good performance, retrieving on average 49.1% and 35.5% of the time the
best solution for instances with 50 and 100 clients respectively. In terms of computational time, the
MPMA needs at most three minutes on average to solve any instance (100 customers). The algorithm
achieves these low times thanks to the log-normal approach presented in section 4.3 and because
simulation is used limitedly. Indeed, the Split with simulation plus the set partitioning model take on
average around a third of the total time for 100 customer instances.
Moreover, during the whole set of experiments it was evidenced the importance of considering the
stochasticity of the parameters for the proposed problem. When the best chromosomes retrieved by
MA2 were decoded in the uncertain environment (using Split with Monte Carlo simulation) they used
more vehicles than the best solutions found by MPMA. Indeed, the CNV of the best MPMA solutions
is just over half of the CNV of the best MA2 chromosomes. Thus, neglecting the stochasticity and
using deterministic solutions can conduce to a huge increment of the number of vehicles. This is
specially important for type 1 instances in which due to tight time windows, deterministic solutions
perform very poorly.
4.5.3 The eects of considering multiple populations
To assess the eects of using multiple populations in the MPMA we compare the performance on 100
customer instances of the single MA1 and the MA1 combined with MA2 and MA3, to the full MPMA.
Table 4.8 presents the results for the dierent congurations. It is shown that using the MPMA with
the three populations improves the solutions in terms of the average number of vehicles by around 6%
when compared to the MA1 used alone. Moreover, the best solutions of the full MPMA reduces by
more than 4% the same metric of MA1, MA1+MA2 and MA1+MA3. In general, congurations with
more than one population achieves to nd solutions with less vehicles than the single MA1. Besides
this, the best costs in terms of distance are achieved by the MPMA with three populations, however
it presents the higher recourse costs. Despite the increase of nearly 15% on the average total time of
the full MPMA in relation to MA1, the former conguration has the best overall results.
4.5.4 MPMA + Log-normal approximation comparisons
To further test the proposed MPMA + log-normal approximation, we compared it to the works of
Miranda and Conceição [34] and Nguyen et al. [39]. These are two of the closest problems related
to the problem at hand. [34] consider the SVRPTW with stochastic travel and service times and
time windows. Also, late services are allowed but the service must start at or after the opening time
window (ei∀i ∈ Vc), furthermore the authors set the customers service level at 80%. Their Iterated
2The number of trials was increased for this test to 30,000 to have a better accuracy on the condence intervals, andthus conclude if two solutions are dierent on average
83
4.5. NUMERICAL RESULTS
Table 4.8: Comparison of single MA1 to MPMA - 100 customer instances
Metric MA1 MA1 + MA2 MA1 + MA3MA1 + MA2
+ MA3
# Vehicles Best Solutions 579 575 576 554# Vehicles Average 616.7 612.7 608.4 577.3Average Distance Cost 1156.56 1158.88 1138.24 1123.87Average Recourse Cost 3.86 3.90 3.88 31.16Avg. Time on pure MA (s) 100 100 100 100Avg. Time on simulation+setpartitioning (s)
35.28 65.06 61.85 54.70
Avg. Total Time (s) 135.28 165.06 161.85 154.80
Table 4.9: MPMA comparison to Miranda and Conceição [34] ILSInstance Method
Avg. #Veh.
Avg.DC
Avg.Min SL
Avg. SLAvg.SLE
Max.SLE
Time(s)
R105 ILS 17.67 1615.75 81.66% 99.31% 0.18% 1.81% 18.40MPMA 17.30 1470.89 80.55% 97.30% 0.39% 2.81% 31.82
R109 ILS 15.00 1488.81 79.53% 95.45% 0.17% 0.89% 6.36MPMA 15.00 1270.27 80.96% 98.00% 0.28% 3.44% 32.21
C101 ILS 17.00 2284.77 83.82% 95.52% 0.22% 0.87% 29.49MPMA 17.00 1372.27 80.42% 95.42% 0.64% 3.56% 31.77
C106 ILS 14.67 1722.32 81.33% 98.65% 0.18% 0.70% 5.56MPMA 14.00 1141.17 80.57% 96.01% 0.72% 4.78% 32.25
RC101 ILS 19.67 2012.76 78.32% 100.22% 0.16% 1.42% 15.18MPMA 19.60 1841.32 80.98% 97.53% 0.46% 4.98% 31.77
RC106 ILS 14.67 1584.63 82.26% 95.57% 0.16% 0.82% 24.43MPMA 15.30 1534.95 81.07% 97.87% 0.40% 4.64% 32.08
RC107 ILS 14.00 1569.10 83.03% 96.23% 0.16% 0.86% 34.99MPMA 13.70 1368.53 81.46% 98.44% 0.23% 3.01% 32.40
Avg. ILS 16.10 1754.02 81.42% 97.28% 0.18% 1.05% 19.20MPMA 15.99 1428.49 80.86% 97.20% 0.44% 3.89% 32.04
Local Search (ILS) is used to solve some of the Solomon [41] instances with 100 customers. Service
times are assumed to be normally distributed with mean equals to the deterministic service time and
standard deviation derived from a coecient of variation generated by a uniform law U [0.1; 0.6]. Travel
times are also assumed to be normally distributed with mean equals to the euclidean distance between
the nodes and standard deviation derived from a coecient of variation generated by a uniform law
U [0.1; 0.6]. Some modications were made to our MPMA to compare it with the ILS proposed by
Miranda and Conceição [34]. Service levels α, β, γ were set to 80%, 0%, and 0% respectively. No
recourse action is considered for the problem so the cost of the route (equation 4.7) only takes into
account both distance and xed costs of the vehicle. Travel and service times were modeled as normal
variables same with the same parameters used in [34]. The MAs stopping condition is set to 25
seconds running time and late services are allowed as in [34]. Table 4.9 presents the results of the
two methods, which are gathered over 10 runs. For each instance the method, the average number
of vehicles (Avg. # Veh.), the average distance (Avg. DC), the average minimum service level (Avg.
Min SL), i.e. minP (AT rj ) ≤ li | rj = i, i ∈ Vc, the average service level for all customers (Avg. SL),and the average and maximum service level error (Avg. SLE - Max. SLE) are reported. Service level
errors are dened as the absolute dierence between service level estimated by simulation minus the
service level calculated with the approximation.
Results show that MPMA uses near to one percent fewer vehicles than ILS. This decrease does
not negatively impact the operational costs (distance) which are improved upon by almost 23%.
Concerning the service levels, the two methods appear to give similar results to the extent that the
average service level of MPMA is inferior to ILS by only 0.08%. Moreover, it should be noted that
MPMA ends up using Monte Carlo simulation enabling the method to validate the chance constraints.
In fact, MPMA guarantees a service level of α in all tested instances rather than the ILS method which
guarantees this constraint in only ve out of seven instances. In terms of service level errors, the metric
tends to be higher in MPMA although solutions with a higher number of vehicles and distance cost
have a signicant reduction in service level errors. It cannot be concluded which approximation
performs better using the service level errors as far as this metric is gathered from the best solution
84
CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
Table 4.10: MPMA comparison to Nguyen et al. [39] TSInstance Method Avg. # Veh. Avg. DC Avg. ET Time (s)
C1 TS (AD1) 10.33 944.55 2.82 9.20TS (AD2) 10.33 929.85 3.43 7.94TS (AD3) 10.22 918.61 4.19 8.62MPMA 9.89 908.73 5.55 51.41
C2 TS (AD1) 3.25 654.41 0.00 14.34TS (AD2) 3.13 648.60 0.04 9.97TS (AD3) 3.00 638.40 0.02 8.52MPMA 3.00 606.30 0.08 51.60
R1 TS (AD1) 15.67 1521.12 30.77 16.10TS (AD2) 15.83 1495.02 24.02 14.96TS (AD3) 15.58 1447.70 26.02 18.95MPMA 15.51 1409.47 19.25 51.76
R2 TS (AD1) 3.45 1134.96 5.33 51.50TS (AD2) 3.55 1050.33 1.48 42.38TS (AD3) 3.45 1086.00 1.77 43.90MPMA 3.99 991.07 1.96 53.56
RC1 TS (AD1) 15.25 1736.57 49.79 11.24TS (AD2) 15.38 1713.98 34.81 10.78TS (AD3) 15.13 1700.99 41.07 9.46MPMA 15.86 1630.91 22.55 51.71
RC2 TS (AD1) 4.00 1396.98 4.95 33.49TS (AD2) 3.88 1296.20 2.64 25.82TS (AD3) 4.00 1234.10 3.17 23.41MPMA 4.56 1182.94 3.86 52.67
Avg. TS (AD1) 8.91 1241.83 15.91 23.48TS (AD2) 8.95 1198.80 11.35 19.46TS (AD3) 8.82 1181.67 12.92 19.98MPMA 9.04 1131.34 9.19 52.16
found by each algorithm at each run, and they are also structurally dierent (see operational costs).
Computational times were not scaled up since operating systems, language programming and hardware
characteristics were dierent, however, it can safely be said that ILS is faster than MPMA using nearly
40% less time.
Nguyen et al. [39] work on the SVRPTW with stochastic travel times and time windows, using
a Satiscing Measure Approach (SMA) to guide its solution process. The authors use a tabu search
(TS) with their SMA and tested it on Solomon [41] instances. Travel times are assumed to follow
an ambiguous distribution, however, for simulation purposes, Gamma distribution is used with a
mean equivalent to the deterministic distance between two nodes and standard deviation derived
from a coecient of variation of 0.5. Hierarchical objectives are used by the authors, in descending
order of importance: maximize the total number of customers served, maximize the overall satiscing
measure, minimize the total number of vehicles used, and minimize total distance traveled. The
following changes were made to our MPMA by aiming to compare it with the results in Nguyen et
al. [39]. Service levels α, β, γ were set to 50%, 0%, and 0% respectively since these constraints are
not considered in [39], therefore we let MPMA be driven as an SPR. Travel times are assumed to
be Gamma distributed with the same parameters used in [39]. The α value of 50% serves to discard
routes with high probability of failures and thus to accelerate the MPMA local search. Furthermore,
as a result of the associated travel times variability is high, the MA3 is set to the 60th percentile that
is tij = φij−1 (60%) ∀ [i, j] ∈ E where φij−1∀ [i, j] ∈ E represents the inverse probability functions of
the travel times. The MAs stopping condition is set to 40 seconds running time and late services
were allowed as in [39]. Table 4.10 presents the summary of results gathered for MPMA (out of 10
runs), and TS (out of 1 run). TS runs only once since it does not have any randomized components.
For each family of instances the method, the average number of vehicles (Avg. # Veh.), the average
distance or expected travel time if travel times are set to their mean (Avg. DC), the average expected
tardiness (Avg. ET), and the average running time (Time) are reported.
Results show that MPMA is very competitive. If compared to TS (AD1) the MPMA increments
the number of vehicles by only 1.5%, additionally MPMA can reduce distance costs by almost 10%
and more importantly, reduces the expected tardiness by more than 40%. The MPMA presents
the best results among the compared methods in both distance (total travel time) and expected
85
4.6. CONCLUSIONS
tardiness. Although MPMA needs more time than TS, it nds better results on average, with low
expected tardiness and barely incrementing the number of vehicles used. Times are not scaled up
since dierences in the operating system, language programming, and hardware, yet, TS procedures
work on average 2.5 times faster than MPMA.
4.6 Conclusions
This chapter presented a Multi-population Memetic Algorithm (MPMA) to solve vehicle routing
problems with time windows (SVRPTW) and stochastic travel and service times. The approach
incorporates the fact that failures can take place at the same time that a service level must be satised.
The estimation of the arrival times is based on the assumption that they can be approximated by a
log-normal distribution.
The MPMA exploits the characteristics of dierent populations guaranteeing high service levels
for customers service. The MPMA was tested by comparing it to two recent variants of the SVRPTW
where late services are permitted. The MPMA found on average better solutions for the two compar-
isons albeit MPMA has higher computational times.
The results conrms that our formulation and resolution method are exible enough to deal with
some variants of the SVRPTW and that the MPMA + log-normal approximation is a valid and
eective method to solve SVRPs. Undergoing work is concentrating on the use of the proposed
approximation within exact methods and a multi-objective version of the algorithm where service
levels are considered as objectives rather than constraints.
Contributions
Preliminary results of this chapter were presented at MIM2016 conference:
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2016)
A multi population memetic algorithm for the vehicle routing problem with time windows and
stochastic travel and service times
In 8th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2016 Troyes,
France, 2830 June 2016
Extensions of the problem at hand were also presented at CLAIO2016 conference:
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2016)
An approximate column generation for the vehicle routing problem with hard time windows and
stochastic travel and service times. In XVIII CLAIO, the Latin-Iberoamerican Conference on Opera-
tions Research Santiago de Chile, Chile, 26 October 2016.
An article version of this chapter has been published in the Computers & Industrial Engineering
journal. Please cite it as follows:
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2018). A multi-population algorithm to solve
the VRP with stochastic service and travel times. Computers & Industrial Engineering, 125, 144-156.
https://doi.org/10.1016/j.cie.2018.07.042.
MPMA Tables with average results for Solomon instances
86
CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
Table4.11:50
Customer
instancesbestandaveragesolutionsfoundby
MPMAper
instance
BestSolution
Average
Instance
TotalCost
#Vehicles
Distance
Recourse
TotalCost
#Vehicles
Distance
Recourse
Avg.Time(s)
C101
9741.22
9.00
642.90
98.32
10568.66
9.90
643.79
24.87
33.87
C102
8635.27
8.00
590.81
44.46
9250.17
8.63
592.84
32.33
35.02
C103
7534.46
7.00
514.58
19.88
7643.30
7.10
518.78
24.52
37.20
C104
5382.54
5.00
369.45
13.10
5391.23
5.00
376.50
14.73
42.13
C105
7555.61
7.00
522.88
32.73
7557.89
7.00
518.46
39.43
37.31
C106
8609.68
8.00
564.34
45.34
9280.85
8.67
583.46
30.73
34.76
C107
6506.86
6.00
484.18
22.68
6721.72
6.20
473.09
48.63
38.69
C108
6467.17
6.00
440.31
26.86
6471.70
6.00
441.35
30.35
39.13
C109
5369.22
5.00
364.11
5.11
5369.68
5.00
364.40
5.28
40.41
C201
3432.76
3.00
417.42
15.34
3437.15
3.00
421.77
15.38
55.02
C202
2462.64
2.00
433.43
29.20
2941.41
2.50
418.47
22.94
59.62
C203
2514.33
2.00
501.01
13.32
3227.11
2.80
410.43
16.68
59.12
C204
2384.27
2.00
372.21
12.06
2387.06
2.00
372.09
14.97
67.22
C205
2464.49
2.00
460.92
3.57
2745.95
2.30
435.88
10.06
59.35
C206
2460.33
2.00
456.51
3.82
2461.65
2.00
456.44
5.22
64.03
C207
2449.09
2.00
448.27
0.82
2916.79
2.50
412.22
4.57
58.92
C208
2366.15
2.00
364.61
1.54
2366.67
2.00
365.46
1.20
64.69
R101
16232.47
15.00
1191.51
40.96
16251.20
15.00
1193.00
58.20
30.37
R102
14082.91
13.00
1049.13
33.78
14387.81
13.30
1054.86
32.96
32.37
R103
10890.01
10.00
867.52
22.49
10894.64
10.00
873.06
21.58
34.18
R104
7734.53
7.00
722.66
11.88
8341.19
7.60
725.32
15.87
38.77
R105
12007.66
11.00
986.34
21.32
12012.63
11.00
991.21
21.43
32.00
R106
9898.11
9.00
867.16
30.95
9912.10
9.00
874.72
37.38
34.24
R107
8795.05
8.00
775.87
19.18
8806.54
8.00
776.63
29.91
36.06
R108
6659.90
6.00
631.58
28.32
7069.71
6.40
644.84
24.87
40.86
R109
9863.25
9.00
849.35
13.90
9873.92
9.00
850.63
23.29
33.76
R110
8779.34
8.00
762.32
17.02
8795.09
8.00
772.41
22.69
35.21
R111
8751.49
8.00
745.91
5.58
8752.46
8.00
745.84
6.62
35.71
R112
7678.27
7.00
672.68
5.58
7683.13
7.00
672.59
10.54
38.10
87
4.6. CONCLUSIONS
Table4.11:50
Customer
instancesbestsolutionsandaveragefoundby
MPMAper
instance:Continued
BestSolution
Average
Instance
TotalCost
#Vehicles
Distance
Recourse
TotalCost
#Vehicles
Distance
Recourse
Avg.Time(s)
R201
3898.66
3.00
892.30
6.36
4390.74
3.50
880.36
10.38
46.90
R202
3745.80
3.00
739.81
5.99
3751.01
3.00
746.78
4.23
51.04
R203
2726.72
2.00
703.38
23.34
3552.54
2.90
648.06
4.48
53.28
R204
2512.02
2.00
511.28
0.74
2514.82
2.00
513.20
1.62
71.17
R205
2774.60
2.00
768.96
5.64
3748.57
3.00
746.25
2.32
50.60
R206
3642.05
3.00
642.02
0.04
3642.61
3.00
642.54
0.07
52.79
R207
2587.16
2.00
587.16
0.00
2785.60
2.20
585.40
0.20
63.16
R208
2493.69
2.00
493.69
0.00
2495.53
2.00
495.50
0.04
75.37
R209
2694.99
2.00
693.19
1.80
3541.97
2.90
641.44
0.54
53.32
R210
2697.69
2.00
692.05
5.63
3096.76
2.40
691.66
5.11
56.89
R211
2561.32
2.00
561.28
0.03
2565.12
2.00
564.91
0.21
62.85
RC101
12133.54
11.00
1091.58
41.96
12136.53
11.00
1092.18
44.35
32.45
RC102
9947.87
9.00
901.19
46.68
9951.28
9.00
901.64
49.64
34.65
RC103
8883.27
8.00
848.98
34.29
8886.01
8.00
847.77
38.24
36.93
RC104
6797.88
6.00
728.56
69.32
7427.68
6.67
712.08
48.94
40.61
RC105
10989.11
10.00
969.13
19.98
10992.42
10.00
966.46
25.96
33.39
RC106
8935.65
8.00
904.74
30.91
9144.53
8.20
909.40
35.13
35.29
RC107
7827.67
7.00
803.86
23.80
8191.04
7.33
831.58
26.12
36.86
RC108
6738.07
6.00
710.82
27.25
7125.15
6.38
715.42
34.73
39.23
RC201
4825.24
4.00
820.78
4.46
5431.86
4.70
730.02
1.84
40.38
RC202
3836.37
3.00
830.57
5.80
4527.63
3.80
725.19
2.44
45.76
RC203
3640.20
3.00
633.32
6.88
3846.56
3.20
643.27
3.30
49.40
RC204
2498.75
2.00
490.95
7.80
2501.56
2.00
491.83
9.73
70.03
RC205
4684.67
4.00
676.94
7.73
4722.71
4.00
712.35
10.35
42.31
RC206
2914.94
2.00
856.71
58.23
3594.25
2.90
688.42
5.82
51.52
RC207
3617.36
3.00
614.62
2.74
3623.11
3.00
620.07
3.04
51.93
RC208
2531.56
2.00
525.87
5.68
2836.99
2.30
530.66
6.33
60.63
Total
340845.92
302.00
37763.70
1082.22
354543.96
316.27
37254.92
1022.37
2586.86
88
CHAPTER 4. VEHICLE ROUTING PROBLEM WITH STOCHASTIC TRAVEL AND SERVICETIMES
Table4.12:100Customer
instancesbestsolutionsandaveragefoundby
MPMAper
instance
BestSolution
Average
Instance
TotalCost
#Vehicles
Distance
Recourse
TotalCost
#Vehicles
Distance
Recourse
Avg.Time(s)
C101
21519.83
20.00
1497.54
22.29
21521.67
20.00
1492.13
29.54
122.91
C102
18301.69
17.00
1330.67
61.02
18408.91
17.00
1355.34
53.58
126.97
C103
15227.97
14.00
1193.63
34.33
15810.35
14.56
1200.19
54.61
135.82
C104
11986.00
11.00
961.25
24.75
12746.69
11.71
1005.74
26.66
139.63
C105
16292.06
15.00
1199.72
92.35
16635.03
15.33
1210.43
91.27
132.15
C106
16259.28
15.00
1212.13
47.15
16661.56
15.38
1239.61
46.95
128.98
C107
14171.11
13.00
1095.82
75.29
14299.95
13.11
1100.08
88.76
132.54
C108
13069.71
12.00
956.64
113.07
13084.16
12.00
986.37
97.80
134.11
C109
10846.15
10.00
836.91
9.24
10847.90
10.00
837.01
10.89
136.14
C201
5821.75
5.00
717.08
104.67
6034.01
5.20
757.33
76.68
182.17
C202
5755.96
5.00
718.78
37.18
5766.79
5.00
715.54
51.25
182.42
C203
4715.41
4.00
679.56
35.85
4720.62
4.00
683.64
36.98
188.48
C204
4667.72
4.00
654.73
12.99
4673.33
4.00
656.36
16.97
195.69
C205
4688.49
4.00
671.74
16.75
4692.55
4.00
667.81
24.74
188.10
C206
4666.89
4.00
656.51
10.38
4668.69
4.00
658.98
9.71
193.42
C207
4655.02
4.00
649.24
5.78
4656.70
4.00
644.04
12.66
187.30
C208
4632.91
4.00
625.22
7.69
4646.78
4.00
641.82
4.96
192.96
R101
28960.67
27.00
1934.28
26.38
29874.72
27.90
1936.55
38.17
117.27
R102
25744.05
24.00
1725.49
18.56
25771.42
24.00
1746.29
25.13
120.44
R103
19409.84
18.00
1371.98
37.86
19524.64
18.10
1379.08
45.55
124.89
R104
14154.45
13.00
1128.20
26.25
14581.18
13.40
1147.77
33.41
134.78
R105
19602.65
18.00
1527.25
75.40
19950.73
18.33
1530.22
87.18
121.79
R106
17420.11
16.00
1369.40
50.71
17640.71
16.20
1378.77
61.94
126.33
R107
14258.45
13.00
1194.59
63.86
15033.80
13.78
1212.86
43.16
130.68
R108
12068.34
11.00
1040.52
27.82
12954.66
11.90
1036.49
18.16
139.89
R109
15308.71
14.00
1270.43
38.28
16318.27
15.00
1283.37
34.90
126.05
R110
14239.67
13.00
1193.42
46.25
15020.88
13.80
1194.13
26.75
128.51
R111
14184.60
13.00
1151.82
32.78
14642.54
13.44
1165.17
32.92
130.73
R112
13035.89
12.00
1029.60
6.29
13042.87
12.00
1034.17
8.70
135.19
89
4.6. CONCLUSIONS
Table4.12:100Customer
instancesbestsolutionsandaveragefoundby
MPMAper
instance:Continued
BestSolution
Average
Instance
TotalCost
#Vehicles
Distance
Recourse
TotalCost
#Vehicles
Distance
Recourse
Avg.Time(s)
R201
6333.61
5.00
1306.96
26.65
7657.52
6.40
1239.12
18.40
150.98
R202
6090.63
5.00
1079.22
11.41
6628.18
5.50
1112.76
15.42
162.23
R203
4927.94
4.00
916.71
11.23
5589.65
4.67
914.40
8.58
184.70
R204
3792.57
3.00
792.53
0.03
4674.21
3.90
771.75
2.46
212.88
R205
6011.41
5.00
1011.05
0.36
6238.60
5.20
1034.17
4.43
166.24
R206
3981.86
3.00
981.50
0.35
4834.63
3.90
931.97
2.66
190.35
R207
3835.66
3.00
835.66
0.00
4392.29
3.56
834.28
2.45
206.23
R208
3715.32
3.00
710.28
5.04
3722.45
3.00
718.06
4.39
236.11
R209
4926.19
4.00
918.85
7.34
5713.55
4.80
910.65
2.90
171.43
R210
4993.53
4.00
992.32
1.21
5750.90
4.78
967.41
5.72
173.25
R211
4778.41
4.00
778.20
0.20
4793.44
4.00
790.98
2.46
192.24
RC101
20920.01
19.00
1819.27
100.73
21592.35
19.67
1825.71
99.97
121.58
RC102
18714.96
17.00
1660.41
54.55
18949.66
17.22
1665.70
61.74
124.16
RC103
15475.45
14.00
1427.97
47.48
15490.04
14.00
1441.47
48.57
128.98
RC104
13300.61
12.00
1260.09
40.51
13309.50
12.00
1264.60
44.91
137.39
RC105
18783.43
17.00
1704.47
78.96
19486.15
17.70
1718.26
67.89
122.96
RC106
16627.30
15.00
1597.60
29.79
17481.11
15.89
1559.34
32.88
125.24
RC107
15433.93
14.00
1387.96
45.97
15641.16
14.20
1411.37
29.79
128.74
RC108
14321.21
13.00
1309.87
11.34
14591.50
13.25
1313.79
27.72
132.16
RC201
7402.04
6.00
1387.02
15.02
8256.29
6.90
1344.75
11.55
150.34
RC202
6184.45
5.00
1181.73
2.72
6735.37
5.50
1226.10
9.26
163.14
RC203
5098.22
4.00
1096.44
1.78
6122.41
5.11
1005.18
6.12
172.14
RC204
4801.08
4.00
800.55
0.53
4810.54
4.00
808.65
1.89
204.69
RC205
7245.54
6.00
1229.47
16.07
7894.78
6.67
1214.48
13.63
152.72
RC206
5229.15
4.00
1195.54
33.60
6146.14
5.00
1133.96
12.19
166.81
RC207
5071.58
4.00
1067.12
4.11
5825.83
4.78
1036.10
11.95
171.22
RC208
4879.98
4.00
866.94
13.04
5405.15
4.56
844.39
5.20
183.62
Total
618334.60
554.00
62829.36
1505.24
641965.52
577.28
62936.72
1745.07
8668.88
90
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[46] S. M. Turner, W. L. Eisele, R. J. Benz, and D. J. Holdener. Travel time data collection handbook.
Technical report, Texas Transportation Institute and Federal Highway Administration, 1998.
[47] T. Vidal, T. G. Crainic, M. Gendreau, and C. Prins. A unied solution framework for multi-
attribute vehicle routing problems. European Journal of Operational Research, 234(3):658 673,
2014.
[48] X. Wang and A. Regan. Assignment models for local truckload trucking problems with stochastic
service times and time window constraints. Transportation Research Record: Journal of the
Transportation Research Board, 1771:6168, 2001.
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93
BIBLIOGRAPHY
94
Chapter 5
Wind farms maintenance
Let us be careful! If in our climates, the industry can avoid to use the direct solar heat,
necessarily, a day will come when, for the lack of fuel, it will be forced to return to the work of
other natural agents. That the deposits of coal and petroleum will still supply for a long time their
caloric power, we do not doubt it. But these deposits will undoubtedly exhaust themselves...(about
coal energy) One can not refrain from concluding that it is prudent and wise not to fall asleep with
respect to this in a misleading security
Augustine Mouchot
5.1 Introduction
Wind energy is one of the most important sources of renewable energies. According to the Global
Wind Energy Council (GWEC) [17], the year 2016 nished with an installed capacity of nearly 487
GW, representing a 12.6% growth when compared to 2015. In 2016 wind power also achieved to
account for almost a quarter of the worldwide renewable energies capacity (including hydropower)
[65]. Moreover, the GWEC projections show that wind energy installed capacity will increase by
almost 70% in the following ve years reaching nearly 830 gigawatts (GW). Despite the rapid growth
of oshore installations [17] onshore still represents nearly the 97.2% of the total wind worldwide
capacity [16] in 2015.
In this context, Operation and Maintenance (O&M) activities represent an important cost [77, 71,
23, 64] in wind farm projects representing as much as 25% to 30% of the total energy production costs
(Ding et al. [23]). In the oshore context Shaee [72] and Raknes et al. [64], state that O&M costs
range from 25-33% of the total life cycle cost. According to Scheu et al. [71], El-Thalji and Liyanage
[27], and Racknes et al. [64], O&M costs include transportation costs, technician salaries and cost
of repair actions and spare parts, as well as loss of revenue caused by production stops. Therefore,
optimizing the O&M activities is a mandatory task to improve the competitiveness of prices and
attractiveness of the wind energy projects.
This chapter is divided in two parts. The rst one starts by making a review about maintenance
activities in wind farms with a focus on operational decision level of analysis (section 5.2). Then,
section 5.4 introduces a multi-objective maintenance scheduling problem in the context of onshore
wind farms. The multicriteria approach is motivated by the presence of dierent stakeholders with
conicting objectives in this kind of applications. The second part explores the strategic level of
maintenance activities in wind farms in section 5.5. Then, in section 5.6 the eects of the operational
scheduling problem within a long horizon and its relation with the maintenance strategy are studied.
The chapter ends in section 5.7 with concluding remarks on the found results and gives some research
clues.
95
5.2. REVIEW ON OPERATIONAL MAINTENANCE ACTIVITIES
5.2 Review on operational maintenance activities
Wind turbines are the systems that allow to transform the kinetic energy in the air into electric energy
[50, 10]. Although wind turbines can be used as a stand alone system to provide the energy (or part of
it) for particular houses, farms etc, most of the capacity is installed in wind farms to produce energy in
an industrial scale [50]. Wind farms are the collection of several turbines working on grids to produce
energy.
Maintenance activities are performed to keep systems working, and to x and prevent possible
failures [73]. The fact that wind farm projects are devised for long periods of time [60] (20 to 40 years)
makes the maintenance and repair activities a central issue to keep the turbines working. Maintenance
actions can include inspection, changes of consumables (greasing, lubrication, oil lters), oil sampling,
re-tightening of the bolts, repair, overhaul, and replacement of parts [5]. According to Manwell [50]
the scheduled and unscheduled maintenance reduce the availability1 of wind turbines. Furthermore,
maintenance activities have an inuence in the failure rates of wind turbines, since poorly maintained
turbines can have higher failure rates [15]. Moreover, both maintenance activities and failures imply
losses in production due to downtime [71, 27] and generate important costs (spare parts, technicians,
etc.).
The interaction of multiple actors involved in wind farms has eects on how maintenance activities
are carried. Markard and Petersen [51] recognize ve stakeholders on wind farms projects, namely:
turbine manufacturer, project developer, investor, operator, and load management and power distrib-
utor. The exact conguration of the stakeholders roles depends on each project. Still, dierent actors
can play multiple roles in the project. For instance, an investor can also manage the operation of the
wind farm. Situations in which roles are held by dierent entities might derive in cases where, one
actor pursuing its objectives harms other actors targets.
The challenge raised by improving the maintenance of wind farms has attracted a large number
of academic communities with dierent perspectives on how to achieve this goal. Some studies have
focused on the materials used for the components (particularly the blades to avoid failures) of the wind
turbines [9, 43], wind farm topology [44], fault diagnostics techniques [26, 29, 37], prognostics [81, 45,
13, 85, 82, 12, 91], scheduling of activities and resources [8, 40, 79, 19, 35, 31, 30], or maintenance
strategy selection [81, 14, 11, 62, 68, 25]. These studies aim to improve the maintenance activities
according to criteria in dierent moments of the wind farms life-cycle. Therefore, a powerful way to
classify the dierent scientic contributions in this eld is to consider which decision-making level is
involved.
As in production management, maintenance problems can be categorized in dierent decision levels
according to the time horizon involved. Shaee [72] uses a three-echelon classication of decision-
making with strategic, tactical and operational echelons as its levels. Strategic decisions aect in the
long-term the maintenance activities. In this echelon, one can nd aspects such as the location [67, 89],
layout [2, 32, 88], or the type of maintenance strategy [42, 23, 11, 76]. Tactical level is related to mid-
term decisions which can vary from year to year, e.g. spare parts inventory management [36, 82].
Operational echelon deals with short-term maintenance decisions. Within this, one can include the
scheduling of resources to perform the required maintenance tasks [8, 63, 79, 19, 35, 31, 64].
The attention given by the Operations Research community to the Operational level (short-term
maintenance decisions) focused mainly on the maintenance scheduling problem. As pointed out by
Kovàcs et al. [40], maintenance scheduling plays a major role in the turbines availability and costs.
The rst study presented in this chapter is dedicated to such types of operational problems and starts
with a review of studies dealing with this decision level.
1Refers to the percentage of time that a wind turbine is available to produce power
96
CHAPTER 5. WIND FARMS MAINTENANCE
5.3 Operational maintenance level
Operational decision level makes reference to day-to-day decisions. Within this level, maintenance
planning refers to the problems in which maintenance tasks are scheduled to be performed on a xed
short length horizon. The detailed scheduling may include constrained resources, such as technicians,
vessels, vehicles, etc.
In this context, it is usually assumed that the maintenance actions are known prior to their
execution [8, 40, 31]. Additional information on the tasks such as their release dates and due dates
[72] are also known. Since the horizon is limited to very few days, it is a common practice to assume
that stochastic parameters such as wind speed or the waves height can be perfectly predicted. In cases
where parameters can change, a roll over approach is used, re-optimizing each current day scheduling
with the new predictions of stochastic parameters and the realizations over previous periods.
Table 5.1 shows a summary of the principal works dealing with the maintenance scheduling prob-
lem. It focuses on the types of objectives used in the dierent literature works. The column costs
makes reference to objectives associated with costs, while the energy one refers to objectives associated
with the energy production. The following conventions are used for the table: Onshore (ONS), o-
shore (OFF), penalties (PEN), transportation (TRAN), technicians wage (TWAG), technicians extra
wage (EWAG), production loss (PL), energy produced (EP), and revenue (REV).
From table 5.1 one can see that few onshore works have relied on energy objectives. In this context,
Kovàcs et al. [40] assume that crews formed by two technicians are used to perform maintenance tasks.
A Mixed Integer Linear Problem (MILP) on a rolling horizon is designed to minimize the total loss of
production due to downtime of the turbines, or when they are still working but in a degraded state.
Two types of degradations are considered, general and peak, the rst one diminishes the power output
by a percentage in any operation condition, while the second one reduces the production during high
speed winds but not on lower speed ones. Albeit no detailed computational results are given, the
authors claim to solve instances with up to 50 maintenance tasks, 4 teams, and 7 wind farms. More
recently Froger et al. [31] have also considered the onshore wind farm maintenance scheduling. The
authors dealt with multiple technician skills, dierent types of execution modes for the tasks, as well
as dierent farm locations. Moreover, the objective in the proposed model is to maximize electricity
production over a short-time horizon. It must be noticed that maximizing the revenue and the energy
produced are only equivalent when the energy prices are constant. Since this is the case in [31, 30] both
objectives are selected. To solve the problem, two formulations based on Integer Linear Programming
(ILP) are proposed. A constraint programming large neighborhood approach is devised to solve the
problem. The same authors worked a second paper dealing with this previous problem. They tackled
it in this last study [30] with a branch-and-check approach. This method can consistently produce
optimal or near optimal solution for instances with up to 80 tasks, several modes, skills, and farms
locations.
97
5.3. OPERATIONAL MAINTENANCE LEVEL
Table5.1:
Summaryof
windfarm
smaintenance
schedulingworks,objectives,andtypes
ofmodels
Con-
text
Costs
Energy
Reference
ONS
OFF
PEN
TRAN
TWAG
EWAG
PL
EP
REV
Typeof
model/SolutionApproach
Results
Com
ments
Kovacset
al.[40]
xx
MILP
Optimal
Twotypes
ofwindturbines
degradations
Froger
etal.[31]
xx
xMILP/ConstraintProgram
ming
Optimal
Tasks
modes
denetheirduration
Froger
etal.[30]
xx
xMILP/Brand-and-check
Optimal
Instanceswithup
to80
tasks,severalmodes,skillsandlocationssolved.
Besnard
etal.[8]
xx
xx
MILP/Simulation
Approximated
Transportation
cost
reducedthanks
toopportunisticmaintenance
Kennedy
etal.[39]
xx
xx
MILP/G
eneticAlgorithm
-Simulation
Approximated
Costscanbereduceddelaying
thescheduleof
preventive
tasks
Stålhane
etal.[79]
xx
xx
MILP
Optimal
Noweather
modeling.
Daiet
al.[19]
xx
xx
MILP
Optimal
Limited
toinstanceswithless
than
8tasks
Rakneset
al.[64]
xx
xx
MILP-Simulation
Optimal/A
pproximated
Simulationisused
toaddressadynamicversionof
theproblem
Iraw
anet
al.[35]
xx
xx
xMILP
Optimal
Use
ofmultipleO&M
bases
98
CHAPTER 5. WIND FARMS MAINTENANCE
In the oshore context minimizing the transportation costs is omnipresent. This can be explained
by the high costs associated to vessels and helicopters. Besnard et al. [8] propose a stochastic
optimization model for wind farms maintenance planning. Stochasticity is incorporated through
scenarios in which the wind, waves and production take dierent values. The model is based on
a rolling horizon which considers opportunistic and corrective maintenance actions. The horizon
planning is constituted of seven days and is discretized in steps of one day. Scenarios are used to
characterize the expected hourly production power, the wind speed and wave height; and the objective
is to minimize production losses, transportation costs, and extra hours penalties. A similar model is
proposed by Kennedy et al. [39]. The authors consider only one vessel and one maintenance team.
Using a Genetic Algorithm, the authors optimize the order and time at which tasks must be carried
out. The objective is to minimize the production losses, transportation and crew costs. Also, it is
shown that a signicant saving (from 13% to 21%) can be achieved when the maintenance schedule is
optimized instead of repairing the turbines as fast as weather conditions allow access to the turbines.
Problems involving more than one vessel are the most common type in oshore context. Two
models based on arc-ow and path-ow formulations are presented in Stålhane et al. [79] for the
routing and scheduling of vessels that perform maintenance tasks at oshore wind farms. The presen-
ted models consider as objective the minimization of transportation, downtime, and penalties costs.
Instances considering a workday and at most eight tasks and ve vessels are solved to optimality.
A similar problem is tackled in Dai et al. [19]. The authors use a four index MILP where vessels
daily availability depends on the type of the vessel and the weather conditions. Numerical results are
provided for instances with eight turbines and an horizon of three days, aiming to minimize the costs
and production loss. A combined vessel routing and maintenance scheduling problem is tackled by
Raknes et al. [64]. The model consider multiple wind farms which are managed by the same O&M
enterprise. Dierent types of vessels are included, some of which can stay oshore for longer periods,
and other need to return to the depot at the end of each shift. The authors propose a large Mixed
Integer model to minimize the sum of production loss, transportation costs, and penalty costs related
to tasks that are not carried out. Furthermore, a dynamic version of the problem is considered dealing
with new task arrivals and updated weather forecasts. The results show the importance of evaluating
the strategies by simulation rather than considering static models in dynamic contexts.
The maintenance routing and scheduling at oshore wind farms is tackled by Irawan et al. [35]
thanks to a model that considers multiple vessels, periods (days), bases, and wind farms. Weather
conditions are considered by dening maximum working hours for a vessel. A Dantzig-Wolfe decom-
position is used, where for each vessel and each period, all the feasible scheduled routes (customers
visits) are generated. The time horizon varies from three to seven days, with three types of technicians.
The objective function contains the cost of technicians, transports, and penalty costs when turbines
are visited after a given deadline. Results are presented for literature instances with eight turbines
and three periods. The authors also show that O&M with multiple bases and attending multiple wind
farms can produce savings of around 12% when compared to solutions with single O&M base and wind
farm pairs. Yet, it is interesting that Irawan et al. [35] nd that costs are mostly composed by crew
costs. Certainly, that behavior could be expected to appear in the onshore case where technicians are
the most costly resource (apart from spare parts).
From the explored literature almost all the works have used exact methods to solve the problems.
This presents the drawback of ignoring the stochastic parameters to have tractable models. Con-
sequently, the works assume that weather conditions, or the time to perform a task are known in
advance. Moreover, no work has addressed the multi-objective case in which both minimization of the
costs, and the maximization of energy related objectives are tackled at the same time.
99
5.4. A MULTI-OBJECTIVE APPROACH TO THE MAINTENANCE SCHEDULING PROBLEM
5.4 A multi-objective approach to the maintenance scheduling
problem
Based on the considerations presented in sections 5.2, in this section a model and a resolution approach
are presented to deal with the multi-objective maintenance scheduling problem for onshore wind
farms. It is assumed that O&M operator and investors are the two parts involved in the maintenance
scheduling. O&M operator is paid to perform the maintenance activities, and it is assumed that he
seeks to minimize its costs. Meanwhile, the investors expect to maximize the energy production. The
problem is forthwith formally dened.
5.4.1 Problem Denition
A set of J turbines indexed by j are considered. Turbines might be distributed among dierent wind
farms but it is assumed that they are close enough to be reached in despicable times. Furthermore,
turbines might require more than one type of maintenance. Each task i ∈ I represents a maintenanceactivity. It is assumed that the set of tasks is specied prior to solving the problem. Also, each task
is associated with a turbine j ∈ J , and Ij dene the subset of tasks to be performed in turbine j.
To execute each task i a number of χis technicians with the skill s are necessary. Actually, each
task requires one or dierent skills held by technicians to perform the maintenance, e.g. mechanical,
electrical, electro-mechanical, etc. S denes the whole set of skills. Besides, an execution time βicharacterizes every task i. Time βi corresponds to the time elapsed from stopping the turbine until
it is restarted and veried again. It is assumes that all maintenance activities in I requires that the
associated turbine is stopped during the task execution. Additionally, every started task is performed
until nished, and all tasks must be performed during the planning horizon.
Furthermore, every task is associated to a time window [ei, li], where the maintenance should take
place. The opening time window is a hard constraint, so the task must start at ei or latter. The
enforcement of this condition accounts for the necessity of spare parts or any special equipment (e.g.
cranes) to perform the task. Other spare parts or consumables are assumed to be available at ei. The
time window closure is a soft constraint; therefore, the task should preferably be nished at most at
li. In the case it nishes later, a penalization cost α is considered and is proportional to the delay.
Usually, periodic tasks can be performed all along the planning horizon, while corrective maintenance
can be constrained for special equipment or special considerations based on their impacts.
Time is discretized in equally length periods t ([19, 31, 35]). The set of all time periods is dened
by T . Every time period stands for the same amount of time, e.g. one minute, two hours, etc. During
each time period t, a turbine j generates an amount of energy or utility per production denoted as
Θjt. Moreover, the additional representation of time as done by Froger et al. [31] is also used. In
this one, a set D denes the set of days in the planning horizon. Every day d ∈ D has a number
of workable t periods called τd. Furthermore, the time intervals within a day are divided as normal
working and extra working periods. The subset of time periods representing extra working periods is
dened as Te. At the end of each working day in the horizon plan, turbines continue to produce an
amount of energy or utility per production Υjd ∀ j ∈ J until the next day. Besides, for safety reasons,
maintenance task can be only carried out during certain periods of time when weather allows it, e.g.
wind speeds are below a given value. To address this fact, the parameter ρit takes value one if the
safety conditions are met and zero otherwise. If a task i is being performed during time t − 1 and
ρit = 0, the technicians must stop until the safety conditions are met again.
A limited set of technicians is dened by P . Technicians travel to the turbines to perform their
assigned maintenance tasks. All technicians perceive a xed salary wp, which is linearly dependent on
the amount of skills they possess. Furthermore, a technician receives an extra wage ewp for each extra
working hour. It is assumed that a worker with a higher salary will enjoy a higher extra payment, i.e.
100
CHAPTER 5. WIND FARMS MAINTENANCE
wp > wp′ then ewp > ewp′∀p, p′ ∈ P . Besides, the parameter πps takes value one if the technician
p has the skill s and zero otherwise. Let us also dene ls as the minimum wage among the workers
p ∈ P | πps = 1 ∀s ∈ S. That is, the less costly wage for a technician with the skill s. When a
technician is assigned to perform a task, he must be present at the turbine until the task is complete.
Moreover, technicians can only work at one task in each time period.
It is considered that O&M operator costs comprise the spare parts, the special equipment to
perform tasks and the technician's wages. Moreover, three main components are examined. The rst
one is the dierence of wages perceived by technicians assigned to tasks that could be performed by
less costly ones. The second one, is the extra hour wages paid to technicians, and the third one, is a
penalization due to maintenance tasks outside the time window. Spare parts and special equipment
costs are disregarded since they are assumed to be unavoidable by the O&M operator. Hence, the
objective is to minimize the costs of the O&M operator while maximizing the amount/prot of energy
production.
5.4.2 Mathematical model
The problem described in section 5.4.1 is now formalized as an Integer Linear Problem (ILP). The
following variables are thus dened:
xit :
1 if task i is scheduled to begin at time t
0 otherwise
zit :
1 if task i is scheduled at time t
0 otherwise
eit :
1 if task i is nished at time t
0 otherwise
ypi :
1 if resource p is assigned to task i
0 otherwise
vpti :
1 if resource p is scheduled at time t to task i
0 otherwise
uid :
1 if task i is scheduled in day d
0 otherwise
γjt :
1 if turbine j is able to produce energy at period t
0 otherwise
ηjd :
1 if turbine j is able to produce at the end of day d
0 otherwise
ζi : The number of days task i is delayed with respect to bi
Using the abovementioned variables and the characteristics described in section 5.4.1, the ILP is
dened as follows:
maxZ1 =∑j∈J
∑t∈T
Θjtγjt +∑j∈J
∑d∈D
Υjdηjd (5.1)
minZ2 =∑i∈I
∑p∈P
∑s∈S
κiypi(wp − ls) +∑p∈P
∑t∈Te
∑i∈I
ewpvpti +∑i∈I
αζi
∑t∈T
xit = 1 ∀ i ∈ I (5.2)
101
5.4. A MULTI-OBJECTIVE APPROACH TO THE MAINTENANCE SCHEDULING PROBLEM
∑t∈T
eit = 1 ∀ i ∈ I (5.3)
zit = xit + xit−1 − eit−1 ∀ i ∈ I, t ∈ T (5.4)
xi0 + ei0 + zi0 = 0 ∀i ∈ I (5.5)∑t∈T
πpsypi ≥ χis ∀i ∈ I, s ∈ S (5.6)
∑t∈T
zitρit=βi ∀i ∈ I (5.7)
dτd∑t=1+(d−1)τd
zit ≤ τduid ∀ i ∈ I, d ∈ D (5.8)
uid = 0 ∀ i ∈ I, d ∈ D | d < ei (5.9)
ζi ≥ duid − li ∀ i ∈ I, d ∈ D (5.10)
zit + ypi ≤ 1 + vpti ∀ p ∈ P. t ∈ T, i ∈ I (5.11)
vpti ≤ zit ∀ p ∈ P. t ∈ T, i ∈ I (5.12)∑t∈T
vpit ≤Mypi ∀ p ∈ P, i ∈ I (5.13)
γjt ≤ (1− zit) ∀j ∈ J, t ∈ T, i ∈ Ij (5.14)
ηjd ≤ 2− (uid + uid+1) ∀ j ∈ J, d ∈ D, i ∈ Ij (5.15)
xit, zit, eit ∈ 0, 1 ∀i ∈ I, t ∈ T (5.16)
ypi ∈ 0, 1 ∀ p ∈ P, i ∈ I (5.17)
vpti ∈ 0.1 ∀ p ∈ P, t ∈ T, i ∈ I (5.18)
uid ∈ 0, 1 ∀ i ∈ I. d ∈ D (5.19)
γjt ∈ 0, 1 ∀ j ∈ J. t ∈ T (5.20)
ηjd ∈ 0, 1 ∀ j ∈ J. d ∈ D (5.21)
ζi ∈ Z+ ∪ 0 ∀ i ∈ I (5.22)
The objectives in equation (5.1) state that the amount/utility of energy produced is to be maxim-
ized while cost must be minimized2. This last is composed by three parts, the total dierence between
the salaries of technicians assigned to a task and the minimum wage of a technician oering the same
skill. The second part accounts for the extra time periods wages, and the third one is the penalization
for not nishing the task within the time window. Constraints (5.2) and (5.3) ensure that all tasks are
started and nished within the time horizon. The consistance between the start, the execution, and
the end of a task is guaranteed by constraints (5.4), which ensure that tasks are executed until nished
when they have been started. Constraint (5.5) state the initial conditions by considering that no task
starts, nishes, or is assigned during time period zero. The number of technicians with the required
skills to perform each task is imposed in constraints (5.6). These equations have a sense of greater
or equal for some special cases, e.g. consider a task i which requires two skills s1, s2 with a number
of technicians of two and one respectively. If the equation is set to equality two technicians p1, p2both with skills s1, s2 cannot be simultaneously assigned to the task since it will violate the equation
for skill s2. Still, this scenario where p1 and p2 are assigned to perform task i is valid and therefore
the constraint sense is kept. Constraints (5.7) guarantee that the amount of time periods spent on
2Note that minZ2 is equivalent to max−Z2
102
CHAPTER 5. WIND FARMS MAINTENANCE
a task will be sucient to nish it, the parameter ρit ensures that the weather allows to execute
the task during these periods. The coupling between tasks executed during a day and the scheduled
time periods is considered in constraint (5.8). Time windows are dened in constraints (5.9) and the
(5.10), the rst ones safeguard that tasks must start after their associated opening time window, and
the second ones, keep track of the number of days a task is delayed with respect to its time window
closure. Equations (5.11) to (5.13) couple the technicians assignment to a task with its execution
times. The parameter M in constraint (5.13) is used as a big M value. Moreover, constraints (5.14)
and (5.15) determine if a turbine can produce energy at normal time periods, and at the end of the
days respectively. Finally, equations (5.16) to (5.22) x the decision variables.
5.4.3 The Epsilon constraint approach
Multi-Objective Optimization Problems (MOOP) are dened as problems where at least two, often
conicting objectives are to be optimized at the same time. Multi-objective optimization methods
dier from single objective ones in which only one solution is found to minimize/maximize the criterion
at hand. In fact, in MOOP more than one solution can be found, especially when the method used
deals with Pareto-optimization. Thus, solving MOOP is a process of nding the ensemble of solutions
called Pareto ecient or non-dominated. These solutions are those for which no objective can be
improved without worsening at least another objective. The whole set of non-dominated solutions is
called Pareto Front [48].
Several approaches are discussed in the literature to solve MOOP, e.g. weighted global criterion
[52], goal programming [80], epsilon constraints [55], etc. To address the model the last method is
selected. This approach is employed to construct an ensemble of Pareto Ecient Solutions (PES).
In general terms, the epsilon constraint works by iteratively solving single objective problems. Con-
sider the following multi-objective problem maxZ (x) = (z1 (x) , z2 (x) , . . . , zn (x)) , x ∈ Ω. Each
zi(x) ∀ i = 1 . . . n represents an objective, and the condition x ∈ Ω stands for the feasible set of
solutions. Epsilon constraint method solves a group of problems max zj (x) | zi (x) ≥ ∆i ∀ i =
1..n ∧ i 6= j, x ∈ Ω by changing the values of ∆i. Each solution found is ecient and kept devising
(partially) the Pareto Front. Although simpler approaches can be used such as the Weighting Method
in which the dierent objectives are reduced to a simple objective using weights for each compon-
ent, the epsilon constraint is preferred for the following reasons. First, epsilon constraint can nd
non-supported solutions, i.e. solutions not in the convex envelope of the Pareto Front. Second, there
is no need to scale the objectives. Third, epsilon constraint iterations can be coded so new ecient
points are found at each iteration, this can avoid iterations which need considerable amounts of time
for solving the ILPs.
Algorithm 5 Epsilon Constraints adapted method for maximizing Z = (Z1,−Z2)
Require: numSteps1: Solutions← 2: z1∗, z2. ← maxZ1
3: z1∗, z2∗ ← max−Z2| (Z1 = z1∗)
4: Solutions← Solutions ∪ z1∗, z2∗5: z1., z2∗∗ ← max−Z2
6: stepSize← (z2∗−z2∗∗)
numSteps
7: i← 18: while (z2
∗ − i · stepSize) > z2∗∗ do
9: z1∗, z2. ← maxZ1| (Z2 ≤ z2∗ − i · stepSize)10: z1∗, z2∗∗∗ ← max−Z2| (Z1 = z1
∗)11: Solutions← Solutions ∪ z1∗, z2∗∗∗12: i← i+ 113: end while
14: Sort and check(Solutions)
Algorithm 5 shows the general steps of the used implementation of the epsilon constraint method.
103
5.4. A MULTI-OBJECTIVE APPROACH TO THE MAINTENANCE SCHEDULING PROBLEM
The procedures maxZ1 and max−Z2 solve the ILP described in section 5.4.2 maximizing the produced
energy and minimizing the costs respectively. Moreover, the procedures retrieve both the values of
Z1 and Z2 given the solution of the ILP. Line 1 starts by initializing an empty array in which the
solutions will be kept. In line 2, the quantity/utility of the energy production is maximized, while line
3 minimizes the costs with the additional constraint that energy production must match the one found
in line 2. The solution is then saved in the proper array of ecient solutions. It shall be noticed that
it is necessary to solve both problems (energy and costs), to retrieve an ecient point. Preliminary
tests show that solving only the problem maxZ1 gives suboptimal solutions in terms of costs. The
algorithm continues solving the problem max− Z2 in line 5, this is done to dene the interval within
the objective Z2 is comprised, i.e. [z2∗, z2
∗∗]. In line 6, a step size is calculated to use it for the epsilon
constraint method. This value depends on the minimum and maximum value attained by Z2 as well
as a parameter called numSteps. This last allows to control the number of iterations performed during
the loop. Higher values for the number of steps permit to better determine the Pareto Front at the
expense of higher computational times. Nevertheless, the value for this parameter does not guarantee
that a dierent ecient point will be found for each possible step value, it works as an upper bound.
Between lines 8 and 13 the main loop iteratively solves the ILPs for energy and costs using the proper
constraints, while saving the ecient solutions. Finally, in line 14 the ensemble of solutions is sorted
and solutions are checked for dominance. If solutions are optimal, they are ecient, nevertheless, if
optimality is not guarantee, solutions are only potentially ecient.
5.4.4 Results
To test the model, the instances proposed by Froger et al. [31] are used. This testbed is composed
by 160 instances grouped in families (5 instances per family) described as a_b_c_d_e where a, b,
c, d, and e refer to the number of time periods in the planning horizon, the number of periods per
day, the number of skills considered, the number of tasks and technician-to-work ratio, respectively.
Accordingly each core characteristics can have dierent values such as: time horizon lengths (10, 20
or 40), time periods per day (2 or 4), number of tasks (20, 40 or 80), number of skills (1 or 3), and
the technician-to-work-ratio (A and B). Among these, only the subset of 40 instances with at most 20
tasks and 20-time periods are used. Moreover, for instances with two-time periods per day additionally
period to stand for extra hours is considered. This extra time period has an implicit duration of four
hours. The same procedure is performed for instances with four-time periods, however for these last,
two extra time periods are added per day (each one representing 2 hours). To assign an energy utility
to these time periods, the following procedure is employed. The original Υjd for each day and turbine
is taken and divide by 16 (number of hours between workable days). Then, extra periods use this
coecient multiplied by the number of hours they stand for, to determine the amount/utility of energy
production. It is assumed that weather conditions are safe to perform maintenance tasks during extra
time periods, i.e. ρit = 1. Additionally, since original instances consider multiple modes or ways to
perform each task, a single mode is randomly selected. This picked mode includes the information on
the number of technician per skill required to perform the task, and its duration (number of periods).
Besides, it is assumed that transport times between every pair of turbines are negligible. Tasks time
windows are not considered in Froger et al. (2017) instances, thus ei = 0, li = |D| ∀i ∈ I.To constraint the computational eort required to solve the problems the amount of time that
epsilon constraint method expends on the ILPs is limited. For problems in lines 2, 3 and, 5 in
Algorithm 1, the time limit is set to 2000 seconds. ILPs in lines 9 and 10 are limited to 500 seconds.
The rst problems are led to run for more time since they are used to create the interval in which
epsilon constraints will be dened. Moreover, the parameter associated to the number of steps is set
to 50. This value guarantees a good trade-o between the approximation of the Pareto Front and the
running times. Both time limits and the number of steps were selected after several preliminary tests.
Since times are constrained, optimal solutions are not guaranteed, therefore, the GAP metric for each
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CHAPTER 5. WIND FARMS MAINTENANCE
89000
89500
90000
90500
91000
91500
92000
92500
93000
100 150 200 250 300 350
Energy
Costs
Figure 5.1: Approximate Pareto Front for Froger et al. [31] instance 10_2_1_20_B_5
Table 5.2: Epsilon Constraints summary results for Froger et al. [31] instancesFamily Avg. Time (h) Avg. Solutions Avg. E. Solutions Avg. Gap C Avg. Gap E Avg. CNOP Avg. ENOP
10_2_1_20_A 1.85 12.2 4.4 4.7% 0.7% 2 7.210_2_1_20_B 1.22 10.2 4 0% 0.3% 0 610_2_3_20_A 1.96 10.8 5 5.7% 0.6% 2 4.610_2_3_20_B 2.39 13.4 6.2 9.5% 0.3% 1.2 620_4_1_20_A 2.81 12.8 4.2 16.2% 0.5% 4.6 7.620_4_1_20_B 3.41 15.2 3 12.1% 0.1% 5.2 10.820_4_3_20_A 2.65 8.4 1 28.4% 1.6% 5.0 5.120_4_3_20_B 2.70 7.8 1 34.6% 1.4% 5.4 4.6
Total 2.37 11.35 3.6 13.9% 0.6% 3.2 6.5
single objective problem is saved.
All runs are conducted on a Dell Latitude E6420 personal computer with Intelr CoreTM i7-
2760QM @2.4 GHz, running Windows 7 Professional 64 bits. The algorithms were coded on Java and
compiled with JavaSE-1.8_45, with maximum allocated memory of 1 Gb. To solve linear problems,
the Java interface with Gurobi 7 (2017) optimizer is used.
Figure 5.1 presents an example of the solutions found. The instance originally contains 10 period
times but this value was increased to consider extra time periods. Three periods are dened for
each day and only one skill is considered. Moreover, the instance contains 20 tasks with a regular
technician-to-work-ratio, that is, technicians can perform all the tasks during the planning horizon.
16 non-dominated points (solutions) are found, although only two are proven optimal, thus ecient.
Moreover, 12 points present a small gap in the energy component. Still, this gap is on average of
0.03%. The other two points present an average gap of 0.04% in the cost component. The graphic
shows an overall concave behavior, displaying a smaller energy utility/production when costs are small
and bigger production at the expense of higher costs. However, it shall be noticed that not all points
rely on the convex hull, e.g. point nine, counting from the left-below part of the graph, showing hence
the conicting nature of the objectives. All the tested instances present a similar behavior in terms of
shape. Furthermore, it shall be noticed that for gure 5.1, an increase of 115% in the minimum cost
can have an impact of almost 4% in the energy utility/production.
Table 5.2 summarizes the results found for testbed instances. For each family of instances is
reported, the average time (Avg. Time) per solved instance, the average number of non-dominated
solutions found (Avg. Solutions), the average number of solutions proven to be ecient (Avg. E.
Solutions), the average gap for non-optimal solutions in terms of costs (Avg. Gap C), the average gap
for non-optimal solutions for the energy objective (Avg. Gap E), the average number of solutions in
which costs are not optimal (Avg. CNOP), and the number of solutions in which energy is not optimal
(Avg. ENOP).
Table 5.2 shows that on average, instances take around 2.37 hours to be solved. A signicant
105
5.5. MAIN CONTRIBUTIONS ON STRATEGIC DECISION LEVEL
increase in computational time is seen when the number of periods is incremented. The reason for
this behavior is the increase in the size of the ILP models. In terms of the number of solutions, a
very limited number is found for the whole set of instances, averaging 11.35 points. A reasonable
explication to this limited number of potentially non-dominated solutions is that solution space of
the problems is highly constrained. Therefore, the number of solutions, and more important, ecient
solutions are limited.
Among the solutions found by the epsilon-constraint method, nearly one third are proven to be
ecient. As little as this number might be seen, this fact can be explained by the complexity in solving
the single objective ILP. One can see that the cost objective is by much the one with the higher gaps,
averaging 13.9%. Furthermore, the gaps on cost component show a considerable rise when more skills
are considered in the problems. This is especially important in instances with higher number of time
periods, for instance family 20_4_3_20_B reaches a maximum of 34.6%.
Energy objective contrary to costs objective, show an excellent performance. Through the 40
instances, the average gap is only 0.6%. Type B instances (regular technician-to-work-ratio) consist-
ently outperform type A (tight technician-to-work-ratio) instances in this metric. Despite the better
performance in terms of energy gaps, results show that the number of solutions not proven ecient is
mostly due to energy objective function. The average number of solutions for which energy presents
a positive gap doubles the same metric for costs objective. Therefore, it is safe to say that single ob-
jective Z2 is solved to optimality more consistently than Z1. However, when dealing with sub-optimal
solutions, the average costs (Z2) gap is over 20 times bigger than the energy (Z1) average gap.
The results reect the importance of operational decisions with the wind farm performance indic-
ators. The fact that schedules can change in as much as 4% the amount of energy produced in the
current context is a matter of most importance. Actually, in closer to real life decisions, the time
required to perform the activities, or the activities themselves might be stochastic. In such situations
the changes in the production can be more pronounced. The same eect may be produced when
bigger instances are solved.
Toward maintenance strategy selection
Evaluating which maintenance strategy is better adjusted is one of the most important decisions
concerning O&M for a project [42, 23, 72, 76]. Having no strategy on how to perform maintenance
tasks usually derives in a very bad performance. Actually, Van Bussel and Schöntag [84] showed
that a maintenance strategy is required to enhance the availability of wind farms. Additionally,
the maintenance strategy policy denes scheduling parameters, in consequence the selection of this
strategy will aect the solution of the maintenance scheduling at operational level. To evaluate the
strategies, this part analyzes the eect of dierent maintenance strategies on the operational cost and
energy production. Before presenting the developed model, a quick revision on strategic maintenance
problems is presented in the following section.
5.5 Main contributions on strategic decision level
Strategies for maintenance activities rely essentially on two main decisions: when to take maintenance
actions and what kind of maintenance (preventive,corrective,opportunistic, etc.) to perform. In the
particular context of wind farms, Shaee [72] uses a general classication based on two major classes:
failure-based (reactive response), and proactive maintenance.
Failure-based strategy uses the idea of performing maintenance tasks only when failures take place,
deriving in corrective actions. This actions can be performed immediately or delayed to a future time
[5, 86]. Nevertheless, there is no standardized way for dening a failure in the wind energy. For
example, Caroll et al. [15] dene a failure as a visit to a turbine, outside the scheduled operation, in
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CHAPTER 5. WIND FARMS MAINTENANCE
which material is consumed. Meanwhile, Wilkinson et al. [87] assume failures as events that require
manual intervention to restart the machine generating a downtime of at least one hour. Consequently,
it is important to dene what failures are in advance so maintenance strategies can be eciently
selected.
Alternatively, Shaee [72] denes proactive maintenance as any maintenance that is performed
before a failure occurs, aiming to avoid possible unexpected failures which usually derive in more
costly actions [41, 53]. Within this classication, two categories can be found: preventive and pre-
dictive maintenance. Preventive maintenance is used to refer to time-based maintenance schemes,
whereas predictive maintenance is equivalent to condition-based maintenance schemes (decision is
made according to the observed health state of the system).
Predictive maintenance usually requires condition-monitoring data. The collected information
used to prevent failures before they become more costly to x. Condition-maintenance-systems are
analyzed in Nilsson and Bertling [59] showing that an increase on the availability of around 0.43% can
compensate the costs of such systems. The eects of false alarm of condition monitoring systems are
studied by May and McMillan [56] showing that even with a decrease in condition monitoring system
reliability, the availability of a wind farm is hardly changed. A recent review on condition-based
maintenance optimization for stochastically deteriorating systems in a general context can be found
in [3].
Prognostics can also be used in maintenance decision making. Examples of maintenance strategies
based on the estimation of the Remaining Useful Life (RUL) of the asset can be found in [12, 46].
Zhao et al. [91] analyze Supervisory control and Data Acquisition Systems (SCADA) information
from turbines to detect normal and anomaly data of turbines generators during their runtime. This
allows to make good estimations of the RUL, and provides sucient time before the appearance of
failures so that a schedule (or a plan) can be implemented before they take place. A recent review on
prognostics techniques for wind turbines can be found at Leite et al. [21].
When dealing with multi-unit systems, another type of maintenance is the one called opportunistic.
This type of maintenance aims to take advantage of breakdowns or stops of the system, due to a failure
or a proactive maintenance, to perform additional proactive maintenance tasks on other components
[81, 22, 12, 1, 47]. In the oshore context, Tian et al. [81] comment the possibility to perform
additional maintenance tasks on nearby turbines when one was selected for a maintenance task. The
main ideas is that grouping maintenance activities can lead to a cost reduction by sharing maintenance
setup costs and downtime duration.
5.5.1 Maintenance strategy selection
Qualitative and quantitative approaches can be used to select the more appropriate strategy according
to Andrawus et al. [6]. The former relies on subjective opinions [7, 70] and may not be adequate
to select the best possible strategy. Quantitative approaches aim to pick the best strategy based on
mathematical models and are by far more studied than the qualitative ones.
In quantitative models, strategies are usually compared together. To evaluate which strategy is
the best, two criteria are often considered: reliability (and availability, see [49]) maximization and cost
minimization [23]. Other metrics such as environmental ones [53] has been used to compare dierent
policies.
Strategies such as failure-based has been studied by Ding [24] given that corrective maintenance
are performed after more than one failure occur. The author shows that this can conduce to an
improvement in terms of cost compared to executing maintenance actions immediately after the rst
failure. Nevertheless, corrective actions have shown to have considerable negative eects [42, 41] on
wind farms costs and availability. Alsyouf and El-Thalji [4] concludes that proactive maintenance is
more suitable for wind farm maintenance problems. Condition-based and opportunistic maintenance
are proposed in Van Bussel et al. [83] adapted to oshore context. Thus, it is safely to conclude that
107
5.5. MAIN CONTRIBUTIONS ON STRATEGIC DECISION LEVEL
relying only on failure-based strategies is far from optimal.
Nowadays, most of the proposed strategies are hybrids in which corrective maintenance is combined
with some proactive maintenance, either preventive and predictive approaches [78]. For example, Lu
et al. [47] studied a condition-based maintenance strategy coupled with opportunistic maintenance
for oshore wind farms. In this work, an articial neural network (ANN) approach is used to predict
the component life percentage based on monitoring information. Also, by using the information from
the ANN, a conditional failure probability is determined. The authors use a two failure probability
thresholds to decide when to perform a preventive or an opportunistic maintenance task. The authors
conclude that the proposed strategy reduces the cost by nearly 30% when compared to a time-based
(preventive) strategy in onshore and oshore contexts. A very similar method can be found in [81], the
authors compare a condition-based maintenance against a periodic maintenance policy. The condition-
based maintenance use two threshold values to decide when the turbines needs to be maintained. The
authors conclude that a condition-based strategy can reduce by more than 40% the maintenance cost
for a ve turbines wind farm example.
Shaee et al. [75] propose an opportunistic condition-based maintenance for wind turbine blades.
The authors optimize two decisions: the length of a crack at which a major repair must be performed,
and the operational age of the blades that triggers a preventive maintenance. In major repairs and
preventive actions, the other blades also receive a maintenance. The proposed method achieves to
reduce by more than 20% the O&M costs when compared to a reactive strategy. A higher saving
(30%) on O&M costs is generated by the proposed strategy against an individual strategy (per blade)
without using opportunistic maintenance. A similar work is presented in Shaee et al. [74] where
the intermediate speed shaft and the high speed shaft are considered. The same parameters (crack
threshold and operational age) are optimized, although, each component has its own threshold. In
Hameed and Vatn [33] the opportunistic strategy is extended to consider components of a turbine and
among turbines. Still, opportunistic maintenance is preferred to be performed on the same turbine to
avoid traveling costs from turbine to another. By using this strategy setup costs are shared among
dierent maintenance activities, thus reducing the total costs. The approach to group the tasks works
as follows. First, an optimal interval for individual components for individual activities is found.
Then, using a heuristics the groups are created. An example of three wind turbines each with eight
components for a horizon length of four years is analyzed. The results show that approximately the
group of activities have a periodicity of around three months. However, further experimentation is
needed to evaluate the pertinence of this type of grouping. Especially, since the appearance of failures
or bad weather times can completely change the planning.
Other studies on strategies selection involving opportunistic maintenance can be found in [22, 69,
92, 90, 28]. In [90] two level thresholds are used to decide on the basis of the reliability functions of
component if minimal maintenance is performed or if a replacement is preferable. Moreover opportun-
istic maintenance is also considered as soon as a minimal maintenance or a replacement takes place.
In Erguido et al. [28] a dynamic opportunistic policy is evaluated. Dynamism is added through reli-
ability thresholds to perform activities, changing in function of the weather conditions. The authors
conclude that using dynamic thresholds surpass the use of static thresholds.
Although the previous works address the comparison of dierent strategies, they usually rely on
many simplications. For example, considerations of the weather, the number of resources (techni-
cians, vessels, etc), the time to perform the maintenance tasks, the necessary spare parts, among
others are not fully addressed issues, or not considered at all. Therefore, other works have focused
on incorporating such considerations so the complexity of wind farms operation and maintenance is
better modeled. In this context, simulation has been the preferred approach to enable more tractable
comparisons.
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CHAPTER 5. WIND FARMS MAINTENANCE
5.5.2 Complex models for maintenance strategy selection
Dierent authors have employed advanced simulation techniques to evaluate maintenance strategies
under the interaction of wind farms elements. Normally, wind farms are simulated as an ensemble
of turbines dened as single or multi-components systems. Moreover, weather models are used to
recreate the wind speeds and wave heights (oshore) faced by the turbines. Weather data has two
purposes, rst it enables to deduct the amount of the energy produced (wind speeds), and second
it allows to check if maintenance safety conditions are met to conduct the tasks. The amount of
energy produced depends on the rating of the turbine, speeds, air density, and many components,
and is usually included as a function or Power Model. Furthermore, the inherent characteristics of
each context creates dierences in the considered elements of the models. For example, maintenance
operations are not constrained by transportation resources when the authors consider onshore wind
farms. Conversely, in oshore this is an important characteristic since dierent types of vessels,
corresponding to scarce resources are needed to perform the maintenance.
Table 5.3 presents a summary of the works using simulations approaches to evaluate maintenance
strategies for wind farms maintenance. These references are chosen dependently in if they consider
limited resources, incorporate weather models, or other operational or tactical levels. The selection
criteria is used as this complex models allow to further research the interaction between strategy
selection and lower levels decision problems. For each work the following nomenclature is used to
classify the main aspects of the works: Onshore (ONS), oshore (OFF), number of turbines (NT),
turbines power generation model (PG), weather model (WM), number of components per turbine
(NC), failures modeling (FM), corrective maintenance (CM), preventive maintenance (PM), predictive
maintenance (PAM), limited technicians (LT), limited resources for transportation (LRT), the presence
of energy metrics to evaluate the strategy (EO), the evaluation of strategies using cost metrics (CO),
and the consideration of inventories (IC). The abbreviation NS stands for non specied.
How the turbines are modeled is an important aspect of the models dealing with how the failures
appear (and are solved). Either a single or a multi-component system are used as seen in column
NC, table 5.3. The former considers only one of the components of the turbine (or aggregate them all
in a single one). Meanwhile, multi-component systems permit to consider the individual degradation
of each selected subsystem, allowing to consider strategies of opportunistic maintenance. Single-
component systems have been addressed by Carlos et al. [14] who propose to model the turbine using
a reliability model in a onshore context. A multi-objective problem is designed by the authors using
maintenance costs and the amount of energy produced as the two selected criteria. The failures process
is considered to be Weibull distributed. Moreover, the authors consider that maintenance activities
are imperfect and use a Proportional Age Set-back as in [54] while Wind velocity is modeled using
a Weibull distribution. Furthermore, Carlos et al. [14] consider that the maintenance time are also
random variables following an uniform distribution. Although the authors do not justify this choice, it
can be explained by the deterioration of the system, or the technicians eciency. Using a simulation
of one turbine, the authors derive an optimum time interval to perform maintenance activities.
109
5.5. MAIN CONTRIBUTIONS ON STRATEGIC DECISION LEVEL
Table5.3:
Summaryof
literaturewithcomplex
modelsformaintenance
strategy
selection
Reference
ONS
OFF
NT
PG
WM
NC
FM
CM
PM
PAM
LTLRT
EO
CO
ICAndrawus
etal.[6]
x26
4Weibulldistribution
xx
xx
xMcM
illan
andAult[58]
x1
xx
4Markovmodel
xx
xCarloset
al.[14]
x1
xx
1Weibulldistribution
xx
xByonet
al.[11]
x100
xx
1Markovmodel
xx
xx
xx
Perez
etal.[62]
x100
xx
4HiddenMarkovmodel
xx
xx
xx
xHofmannandSp
erstad[34]
x50
xx
NS
NS
xx
xx
xx
xSahnounet
al.[66]
x80
xx
1Scalefunction
xx
xx
xx
xDalgicet
al.[20]
x150
xx
NS
Rateper
year
xx
xx
xx
Santos
etal.[68]
x1
xx
4Weibulldistribution
xx
xx
Abdollahzadeh
etal.[1]
NS
29x
x4
Weibulldistribution
xx
xx
xx
Dahaneet
al.[18]
x80
xx
2Scalefunction
xx
xx
xx
xx
110
CHAPTER 5. WIND FARMS MAINTENANCE
Similarly, Sahnoun et al. [66] design turbines as a single component for which degradation is
modeled with ten status scale in an oshore context. Turbines status changes according to a function
that includes, an exponential variable representing the components time to failure, and the eect of
weather on turbines degradation. This is very important since among the studied works, this is the
only one that considers the degradation as a function of the weather. Three strategies are compared
using a multi-agent systems simulation: periodic, condition-based maintenance, and a hybrid version
combining both. Also, maintenance task are scheduled based on rules such as the turbine with the
maximum degradation, or if a time window is available to perform the task. Simulating a wind farm
with 80 turbines, the authors show the hybrid strategy is the most eective considering the costs
and the produced energy. Although Byon et al. [11] also model turbines as one component, this last
stands for a subsystem of the turbine. This contrast with the approach of [14, 66] where the single
component aimed to model the whole turbine. Byon et al. picked the gearbox since the turbines most
critical failures are related to this component. The gearbox degradation is modeled through a Hidden
Markov Chain with a transition matrix of one week periodicity. This type of model allow the authors
to integrate the incomplete information received through sensors output. That is, the real state of the
components are hidden and the sensor outputs can be generated by multiple of these states. Using
a Discrete Events System Specication (based on the work of Perez et al. [61]) they simulate 100
turbines letting to conclude that condition-based maintenance enables more wind power generation
as it reduces the number of failures.
Multi-component systems have been preferred to model the turbines. Half of the reviewed works
use four components to represent the turbines (see column NC, table 5.3). These usually include
the gearbox and the generator, and other components such as the blades, shafts, or electrical ones.
These components are usually selected since they represent most of the downtime experienced by
a turbine [15], or have the larger impacts in terms of costs. Andrawus et al. [6] include turbines
subsystems such as the main shaft, main bearing, gearbox and generator. The last two include also
some sub-components such as the bearings. The lifetime of subsystems and components are modeled
using Weibull distributions. The parameters are derived from maximum likelihood estimators from
reported data failures. A simulation is carried out for 26 wind turbines composing a wind farm.
Maintenance crews are limited and inventories policies are also addressed. Maintenance strategies are
selected for each subsystem based on the parameters of the Weibull distribution, but no comparison
among dierent strategies is given. Meanwhile, the benet of installing condition monitoring systems
for the onshore context is tackled by McMillan and Ault [58, 57]. Similarly to [6], the authors consider
turbines as a four-component model which includes: the generator, gearbox, blades, and electronic
related parts. This study uses the items presenting the most important failures, but also focuses
on monitored components. It is assumed in [58] that monitoring reveals the true component state.
Moreover, the turbine is modeled with a Markov Chain taking account the state of each component.
The authors conclude that for most onshore wind turbine components, condition-monitoring is cost-
eective. Furthermore, according to the authors the same conclusions should hold in the oshore
case.
An extension of the work in [11] is presented by Perez et al. [62] to consider multiple components
in the onshore context. As in other related works [6, 58, 81], the number of components is limited
to four, namely the gearbox, power generator, blades, and control system. Moreover, the authors
include the inherent constraints for schedules due to limited maintenance teams and to lead times in
spare parts. Condition-based-maintenance presents better performance when compared to periodic
maintenance. Additionally, a condition-based-maintenance strategy which also includes opportunistic
maintenance, presents the best results in terms of costs and number of failures. Santos et al. [68]
consider a one turbine model. The turbine is simulated by a four-component that includes the gearbox,
generator, pitch system, and rotor. The authors optimize the maintenance strategies through the use
of generalized stochastic Petri nets coupled with Monte Carlo simulation. The authors optimize two
111
5.6. MAINTENANCE STRATEGIES: RELATION WITH OPERATIONAL PLANNING
parameters: the preventive repair threshold and the age reduction ratio. The rst is a proportion of
the mean time to failure of the component to perform a preventive task, while the second stands for
how much of the age of the component is reduced with the maintenance task.
A multi-objective opportunistic maintenance optimization considering limited resources is tackled
by Abdollahzadeh et al. [1]. Turbines are dened by multiple components (rotor, main bearing,
gearbox, generator) with lifetime modeled by a Weibull distribution. Using the reliability of the
components, the authors dene several thresholds to execute opportunistic maintenance. By mean of
a Particle Swarm Optimization, the threshold values are optimized to maximize the energy produced
while minimizing the maintenance costs. Several conclusions can be retained from this work. First,
it is shown that opportunistic maintenance coupled with preventive actions outperforms the classical
corrective strategies. Second, the number of maintenance teams has an important eect in the Pareto
ecient solutions and the addition of maintenance teams can greatly increase the energy produced.
Third, maintenance strategies must be compared with basis on more than one objective, as far as
these can be in conict and lead to dierent conclusions.
A variant of the work of Sahnoun et al. [66] is introduced by Dahane et al. [18] considering the
impact of spare parts re-manufacturing. Turbines are modeled by two components, a single turbine
representation (as in [66]) and the gearbox. Both elements use similar degradation functions that
include the eects of weather or the accelerated degradation of re-manufactured components. The
comparison is performed on a simulated 80 turbines wind farm. A concluding remark of the authors
is that whichever the strategy, the average production of energy does not vary. The NOWIcob tool
is introduced by Hofmann and Sperstad [34]. The tool uses a multi-component approach, although
the authors do not mention the number of modeled components. The example presented shows
the importance of transportation vehicles in the oshore context. The authors show that the use
of a mothership can increment by more than 3% the availability when compared to the use of a
platform. Dalgic et al. [20] also model turbines as a series of subsystems. Each subsystem is modeled
by its reliability function derived from historical failures rates and expert judgment. Nevertheless,
no information is given about the number of components. The authors also model other uncertain
parameters such as the weather (wind and waves). Although the maintenance strategy is not the focus
of the work, the authors conclude that remote-monitoring (and thus condition-based maintenance)
can lead to important improvements on the oshore wind farms performance metrics.
Overall, the results of [25, 64, 62, 1] show that under the presence of limited resources, maintenance
strategy outputs can signicantly vary. This follows also the conclusion of Van Horenbeek [85] who
claims that assuming maintenance operations duration as negligible can conduce to bad decisions.
Therefore, one can conclude that although models are simplication of the real wind farms, several
aspects must be considered to compare dierent maintenance strategies. Moreover, most of the studied
works consider both the costs and energy related objectives, but only Abdollahzadeh et al. [1] explicitly
construct a set of solutions for which costs and energy production change. It is also interesting to
see how the scheduling of limited resources can be tackled. Nearly all of the cited works schedule the
resources as fast as they can be assigned, ignoring the possible gains (and eects) of optimizing the
resources. Only the work of Sahnoun et al. [66] consider a rule to prioritize some of the tasks. These
two issues, the multiple objectives and the rules to schedule operational resources, are considered in
section 5.6 to evaluate the impact of operational decisions on dierent metrics in a long term horizon.
5.6 Maintenance strategies: relation with operational planning
In this section, the maintenance scheduling problem presented in section 5.4, is embedded in a long-
horizon simulation model to evaluate how costs and energy production objectives perform through the
life cycle of a wind farm project. Nevertheless, since the model and solution approach of section 5.4
requires much time to be practically often solved in a long-horizon evaluation, two actions are taken.
112
CHAPTER 5. WIND FARMS MAINTENANCE
First, the model is simplied to consider only one type of technicians and no extra-hours. Second, it
incorporates straightforward rules to schedule the limited resources. Therefore, these rules are used
to heuristically solve the scheduling problem instead of using an exact solution method.
5.6.1 Problem Description
Let T to be the planning period for a wind farm. During this time the O&M performs its activities
while a set J of turbines produce energy. Each turbine j ∈ J produces PEjt units of energy that
depends on the wind speed during a period t ∈ T . Two dierent strategies to perform the maintenance
are considered: "'On failure mode"' and "Preventive mode" strategies. On failure mode is a reactive
strategy that considers a maintenance tasks if a failure takes place. Besides, the "'Preventive mode"'
considers maintenance tasks based on xed time periods. Furthermore, in this mode if a failure takes
place then a corrective maintenance task is performed. Besides, whichever the strategy, twice a year
the turbines are visited to execute some maintenance on the components not explicitly considered and
to change consumables. The dierent types of maintenance are identied by the set U indexed by u.
Additionally, each turbine j is composed by a set of components K indexed by k. Moreover, turbines
(and their components) are subject to deterioration process which derive in failures. The degradation
process of one component is independent from the others. Turbines components are maintained by
a limited set of technicians (P ) who perform the maintenance. Technicians travel to the turbines to
perform their assigned maintenance tasks within their shifts.
To perform a maintenance task type u on component k, a number of χku technicians are required,
with an associated cost cku. Proactive maintenance is considered as an imperfect repair, that is,
a preventive maintenance action does not restore the component to an as good as new condition.
Conversely, a replacement brings the component back to a state as good as new. Furthermore, an
execution time βku characterizes every task. Time βku corresponds to the time elapsed from stopping
the turbine until it is restarted and veried again. It is assumed in this section that βku is a random
variable with known probability distribution. When a task is started by a set of technicians it is not
stopped until nished, therefore, the assigned technicians will not work on other tasks until they nish
the started activity. Still, at the end of each shift, the technicians stop their work and continue on
the next workable shift. Moreover, if the conditions are not safe (high wind speeds) the technicians
wait until they become safe again.
Maintenance Strategies are evaluated through several metrics gathered from the whole planning
horizon: the average availability (A) of the wind farm, the amount of total energy produced (E),the total number of failures (NF), and the total costs (T C). The objective is to compare the dif-
ferent strategies, taking as decision variables how and when to perform the schedule of the resources
(operational problem). Again, this schedule could be executed with the model presented in section
5.4. Nevertheless, this is computationally intractable due to the length of the planning horizon (and
thus the number of operational problems to solve). For this reason, other characteristics such as the
technicians skills are not considered in this part. Also, to overcome the computational problem simple
priority rules to dene the schedules are devised.
5.6.2 Simulation model
The simulation model is formed by four big modules: the wind turbines, the weather model, the
scheduler model and the resources module. A scheme of the modules is presented in gure 5.2.
Wind Turbines
This module represents the turbines, their degradation process, the time between failures, and the
power generation. It also allows to keep information as availability and energy production. In this
module a set of |J | identical turbines are considered, each of them composed of four components
113
5.6. MAINTENANCE STRATEGIES: RELATION WITH OPERATIONAL PLANNING
Wind Turbines
Weather model
Wind speeds
Energy producedAvailability
Components
Schedule modeler
Resources
Wind speeds
Available
Power Generation
Status
Schedule
Repair
Wind speed –safety conditions
Degradation
CostsNumber of maintenance tasksNumber of failures
Figure 5.2: Scheme of the simulation modules.
(k = 4) namely C1, C2, C3, and C4 (see gure 5.3). Each of the components can be also composed
by sub-components, for instance, the rst component (C1) may represent the blades, which are indi-
vidually represented: SC 1.1 (blade 1), SC 1.2 (blade 2), and SC 1.3 (blade 3). If any sub-component
presents a failure then the component itself will fail, generating a shut-down of the turbine. Thus,
a turbine is designed as a serial connected components. This type of representation allows to model
the turbines as a single component or multi-component system. Furthermore, it enables to explore
dependencies between components such as the eects of one component deterioration over other com-
ponents. Notwithstanding, as explained in section 5.6.1 the model in this part assumes independent
components.
Modeled components
A turbine is modeled by four key sub-components3: the rotor, the main bearing, gearbox and gener-
ator. Such components have proven to be one of the more important cost inductors for maintenance
operations. It is also assumed as reported by [81, 22, 6, 68, 1] that the component times between
failures can be modeled using a two-parameters Weibull distribution. The parameters reported by
Abdollahzadeh et al. [1] for three dierent types of turbines as shown in table 5.4 are used. Since
imperfect preventive maintenance is considered the virtual age vajk of each component k in turbine j
(∀k ∈ K, j ∈ J) [28]. As time pass the virtual age of each component continues to increase. It is only
modied in two cases: if a failure takes place, in which case the component is replaced and the age is
restarted (vajk = 0), and in the case of preventive maintenance. In the second case the new vajk′is
set to vajk′
= vajk (1− q). That is, the virtual age is reduced as percentage of the old age. The term
q (age reduction ratio) can be component dependent. For simplicity in this work it is assumed that q
is the same for every component in every turbine. It is further presumed that q follows a continuous
uniform distribution [14] q ∼ U(0.8, 0.95).
Power generation
Equation (5.23) shows the power generated by a turbine j based on the formulas of Karki and Patel
[38] given a wind speed vt at time t in the turbine location l. Furthermore, v0 represents the cut-in
3Each of the sub-components is not further divided into sub-components
114
CHAPTER 5. WIND FARMS MAINTENANCE
Turbine
C4C3C2C1
SC1.1
SC1.2
SC1.3
SC2.1 SC2.2
SC 2.2.1 SC 2.2.2
SC 4.1
SC 4.1.1
SC 4.1.1.1
Figure 5.3: An example of a turbine modeled a multi-component system.
Table 5.4: Weibull parameters for components failures in Abdollahzadeh et al. [1]Component Scale parameter α (days) Shape Parameter β
Type 1 Type 2 Type 3Rotor (k=1) 3000 2400 1750 3
Main Bearing (k=2) 3750 3100 2400 2Gearbox (k=3) 2400 1200 1200 3Generator (k=4) 3300 2200 1800 2
115
5.6. MAINTENANCE STRATEGIES: RELATION WITH OPERATIONAL PLANNING
wind speed, v1 the rated wind speed, and v2 the cut-out speed.
PEjt :
0, 0 ≤ vt ≤ v0P r(A+Bvt + Cvt
2), v0 ≤ vt < v1
P r, v1 ≤ vt < v2
0, vt ≥ v2
(5.23)
A =1
(v0 − v1)2
[v0 (v0 + v1)− 4v0v1
(v0 + v1
2v1
)3]
B =1
(v0 − v1)2
[4 (v0 + v1)
(v0 + v1
2v1
)3
− (3v0 + v1)
]
C =1
(v0 − v1)2
[2− 4
(v0 + v1
2v1
)3]
Thus, when the weather module provides a speed to each turbine, this one can estimate by means
of equation (5.23) the amount of energy produced.
5.6.3 Weather model - Wind speeds
Performing simulation of wind farms require to address a fundamental aspect, that is, a synthetic
creation of wind data speeds. Several ways of dealing with this are reported in the literature, for
example Carlos et al. [14] use a Weibull distribution to model wind velocity. Byon et al. [11] and
Perez et al. [62] use a spatio-temporal model to generate wind speed sequences at dierent locations
and heights. Meanwhile, McMillan and Ault [58] use an auto-regressive model (AR(1)) to model wind
speeds using the equation (5.24).
wst − µ = Λ (wst−1 − µ) + εt (5.24)
Although the approach presented in [11] seems to oer a more robust way to generate correlated
winds for many locations, the auto-regressive model of McMillan and Ault [57] is kept for its simplicity.
Moreover, it allows to consider the season patterns depending on the month, by varying the value of
Λ, and the value of µ.
5.6.4 Schedule Modeler
Whichever the type of strategy used, at the operational decision level the resources utilization needs
to be optimized. To the best of our knowledge, no other study has before considered to optimize the
maintenance scheduling plan during a life cycle simulation of a wind farm. This is mainly due to
the computationally demanding time to carry on such a task. Certainly, to embed the whole model
and solution approach presented in section 5.4 in a long-term simulation model is unrealizable. This
is why an approximate solution approach based on simple heuristics rules to assign the maintenance
tasks is used.
In this part, failure-based maintenance, and periodic maintenance are compared. Strategy (CM)
performs only maintenance tasks when a failure occurs. Meanwhile, (PM) policy also use corrective
tasks when failures occur, but also, it performs periodic maintenance to avoid possible future failures.
To optimize the schedules a rolling basis to make operational decisions is devised. That is, each
day the assignment of resources to maintenance tasks is redened. Nevertheless, tasks which have
already begun are not rescheduled. Each day, at 7 a.m. it is assumed that the wind speeds for the
116
CHAPTER 5. WIND FARMS MAINTENANCE
Table 5.5: Costs, technicians, and time requirements per maintenance component based on Abdol-lahzadeh et al. [1]
Ck1 Ck2 χk1 χk2 µβk1 µβk2Rotor 28 112 2 3 8 16Main Bearing 15 60 2 3 9.6 16Gearbox 38 152 2 3 16 24Generator 25 95 2 3 12 32
day are known to the O&M operator in a hourly basis. Moreover, next ve days previsions are also
known with certitude. At this time the operator also knows which tasks are needed to be performed
(except for possible imminent failures).
The following rules are tested to design the tasks scheduling:
O1 : schedule the maintenance tasks as fast as possible when there are available resources
O2 : schedule the maintenance tasks to start at times where the wind speeds are low and always
in the morning
5.6.5 Resources
The resources considered are technicians working in a shift starting at 8:00h in the morning and
nishing at 17:00h from Monday to Friday. Technicians perform the maintenance tasks if wind speeds
are below 10 m/s. At higher speeds the technicians need to wait. All other resources as vehicles, spare
parts, cranes or any other requirement are always available to perform the maintenance tasks.
5.6.6 Model implementation
The model described in section 5.6.1 was implemented using a Discrete Events Simulation program
designed for this purpose. All runs are conducted on a Dell Latitude E6420 personal computer with
Intelr CoreTM i7-2760QM @2.4 GHz, running Windows 7 Professional 64 bits. The horizon length is
set to a 15 years horizon length assuming the wind farms working 365 days of the year and 24 hours
per day. A total of 1000 replications are run for each combination of strategy and rule of decision
from the scheduler.
The model is tested on a virtual instance with the following characteristics. A total of 100 turbines
with rated power of 2MW are considered. Each turbine is modeled following the assumptions presented
in in section 5.6.2. For the sake of simplicity, turbines are located in a square location of 9 square
kilometers in a grid with intervals of 300 meters.
Additionally, consider Ck1 and Ck2 to be the preventive and corrective costs for a component
k ∈ K. Table 5.5 presents this costs and the number of technicians (persons) and the expected
time required to carry out the activities. It is assumed as in [1] that βk1 ∼ N(µβk1 , σ2βk1
) and
βk2 ∼ N(µβk2 , σ2βk2
). The values of σ2βk1
and σ2βk2
are inferred from a coecient of variation of 0.2
and 0.3 respectively [1]. Table 5.5 values are gathered from [6, 81, 1, 28] and are reported for Type 1
turbine from table 5.4. In addition, the number of required technicians for the recurrent visits (u = 3)
is two, with a duration uniformly distributed between two and four hours4. It should be noticed that
even if most of the data is gathered from the literature, it is not claimed that it to be representative
for any particular wind farm.
5.6.7 Simulation model results
To gather the results 1000 simulations are run for each of the strategies and scheduling rules: CM-O1,
CM-O2, PM-O1, PM-O2. The "`CM"' strategies are those based on the reactive strategy, meanwhile
"`PM"' use a preventive approach. The "`O1"' and "`O2"' parts stand for the rules presented in
4Based on Carlos et al. [?] and meetings with experts on the eld.
117
5.6. MAINTENANCE STRATEGIES: RELATION WITH OPERATIONAL PLANNING
Figure 5.4: Total maintenance costs by strategy/rule for the wind farm simulation.
section 5.6.4 to schedule the activities as fast as possible or to improve energy production respectively.
Figure 5.4 presents the box-plot for the total costs measured in monetary units (MU) calculated as
the sum of the cost of all maintenance tasks performed during the planning period. It is noticeable
that PM type strategies present lower costs than CM strategies. These results are expected since the
costs of failures are much larger than those due to preventive activities (nearly four times larger, see
table 5.5). Moreover, O2 shows higher costs than O1 specially for the PM strategy. Due to that O2
tries to schedule activities more widespread in time, to exploit low wind periods. This eect might
be also amplied by selecting mornings to schedule the activities and thus, avoid between days loss of
production. Therefore, preventive activities can be executed on larger horizon times (several weeks),
augmenting the probability of failures.
Figure 5.5, conrms that the costs are increased due to the total number of failures. One should also
note that O2 strategies present more variability. This appears to be the result of the vulnerability
of O2 subject to longer scheduling periods. It also oers a clue to derive new rules in which the
uncertainties (appearance of failures) are considered so the plan of preventive maintenance follow an
intelligent order. This analysis lead us to another characteristic to include in the model, the condition
base maintenance strategies. Due to the imperfect maintenance tasks, each component of each turbine
presents dierent degradation levels and therefore, dierent probabilities of stopping working. Such
information can be integrated so new improved rules exploit it to make better decisions.
Figure 5.6 presents the total energy production for all maintenance strategies at the end of the
simulation. The O2 policy seems to oer the higher production. The dierence in the total energy
production between PM-O1 and PM-O2 gives an improvement of nearly 1.7% for the latter. This
is further conrmed with the fact that PM-O2 shows the highest availability (see gure 5.7). The
explanation of this dierences is again, the way in which O2 plans the activities. The avoidance of
scheduling tasks, for example on a Friday afternoon, imply that the turbine will be operative during
the weekend, and the tasks will begin preferably on the Monday.
To further explore the dierences between the tested strategies and scheduling rules, a life cycle
analysis is conducted. The same four metrics (costs, energy production, availability, and number
of failures) are gathered at the end of each year during the simulation of each strategy. Figure 5.8
presents the values averaged from the 1000 runs across 15 years, and for each one the cumulative value
is reported.
118
CHAPTER 5. WIND FARMS MAINTENANCE
Figure 5.5: Total number of failures by strategy/rule for the wind farm simulation.
Figure 5.6: Total produced energy under dierent strategy/rules for the wind farm simulation.
119
5.6. MAINTENANCE STRATEGIES: RELATION WITH OPERATIONAL PLANNING
Figure 5.7: Mean turbine availability for dierent strategy/rules for the wind farm simulation.
0
10000
20000
30000
40000
50000
60000
70000
80000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
TOTA
L C
OST
S (M
U)
YEAR
CM-01 CM-02 PM-01 PM-02
0
100
200
300
400
500
600
700
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
NU
MB
ER O
F FA
ILU
RES
YEAR
CM-01 CM-02 PM-01 PM-02
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
TOTA
L EN
ERG
Y M
WH
YEAR
CM-01 CM-02 PM-01 PM-02
94,00%
95,00%
96,00%
97,00%
98,00%
99,00%
100,00%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
AV
AIL
AB
ILIT
Y
YEAR
CM-01 CM-02 PM-01 PM-02
Figure 5.8: Temporal analysis for wind farm metrics under dierent strategies.
120
CHAPTER 5. WIND FARMS MAINTENANCE
Around the sixth and seventh year of operation, the number of failures start to increase in strategies
that do not use preventive maintenance, this puts in evidence the eect of preventive strategies in
the long term. This fact coincides roughly with mean time to failure of the components (see table
5.4), time at which failures probability becomes more important. The reduction in the number of
failures is reected one or two years on the costs and the variation between strategies becomes more
prominent at the tenth and eleventh year. The lag between failures and costs can be explained by the
preventive costs matching the corrective costs of CM strategies. Nevertheless, after one to two years
PM strategies take the edge and become less costly. Other important aspect to remark is that after
the tenth year the total costs seems to be more stable for the PM strategy while the CM strategy
exhibit a steady increase.
Figure 5.8 shows the behavior of the wind farm availability. This metric is mainly aected by the
scheduling rule, particularly for the preventive strategy PM-O1. Such strategy greatly diminishes the
availability metric around the sixth year of its life cycle. This is due to the preventive behavior of
that strategy since turbines are stopped to execute the maintenance. Nevertheless, after a minimum
value of 95% in the eight year, the availability increases for PM-O1 since very few failures occur.
Meanwhile, strategies based on O2 rule show a more stable behavior, decreasing only by 1% during
the whole horizon-length. Even though O2 allows a bigger availability this also comes with increasing
costs for the PM strategy.
The presented results conrm the idea that maintenance strategies are importantly dependent on
how operational activities are planed and executed. Although the results were gathered for a virtual
example in which resources were highly constrained5 the conclusions might just hold for more realistic
environments. Actually, the reduced number of technicians compensates with the lack of lead times
result of unavailable spare parts or need for special cranes.
5.7 Conclusions
In this chapter Maintenance Planning problems in the wind farm context are presented. The rst part
tackled the operational level problem, in which the resources demanded to perform maintenance tasks
are assigned and scheduled. In the second part the integration between the operational and strategic
level is shown and the eects of dierent maintenance strategies on the operational cost and energy
production is studied.
To solve the maintenance scheduling problem, a bi-criteria mixed integer program is proposed and
solved within an epsilon constraint method for the scheduling problem. The results show that O&M
costs and the energy produced objectives are in conict an have signicant consequences on each
other. Moreover, these results imply that the resources will be assigned depending on the objective
function. Thus, the eects on the long-term of using schedules based on one type of objective need to
be addressed.
Using a Monte Carlo simulation within an Discrete Events framework we compared two classical
strategies widely used in the literature and in real systems: corrective and preventive strategies are
compared. Moreover, two scheduling rules at the operational level are tested within each strategy.
The results shown that the preventive strategy impacts in a positive way the costs and number of
failures comparing with the corrective strategy: the cost are reduced on nearly a quarter and failures
by half. Meanwhile, O2 scheduling rule benets the energy production and availability over rule O1
showing increments of nearly 2% on both metrics at the expense of a 9% increment in costs.
Undergoing work is concentrated on integrating the complete model presented in section 5.4 within
the simulation approach. Moreover, it is intended to incorporate the uncertainties in the scheduling
problem, for example due to weather predictions errors. Besides, new rules to schedule resources and
new maintenance strategies including opportunistic and condition based need to be tested. Finally,
5Only four technicians were considered.
121
5.7. CONCLUSIONS
the studied models will be extended to the oshore case to bring more understanding on the how to
select the best strategy to investigate the maintenance tasks in dierent environments.
Contributions
The results of the multi-objective problem in this chapter were presented at IEOM conference:
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2017)
Wind farm maintenance scheduling model and solution approach
In Proceedings of the International Conference on Industrial Engineering and Operations Management
Bogota, Colombia, 2526 October, 2017.
122
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128
Chapter 6
Conclusions
This thesis is dedicated to the Vehicle Routing Problems (VRP) under uncertainties and Maintenance
Planning on wind farms. Although most of the works on these elds are devoted to deterministic
problems, recently published literature has shown an increasing interest on both research subjects.
This growing attention is due to the fact that in real world applications, data are seldom known
with precision or with certitude when the decisions have to be taken. Furthermore, the eects of
neglecting that data are imperfect when designing maintenance schedule or vehicles routes can have
important impacts in terms of costs, systems unavailability, or customers' dissatisfaction. Within this
context, this thesis makes the following contributions: rst it presents ecient solution approaches
based on memetic algorithms with strategies specially tailored for two Stochastic VRPs variants:
namely the VRP with stochastic demands and the VRP with random travel and service times in
which maintenance tasks on distributed assets must begin within hard time windows. New results are
exhibited to these two stochastic VRPs including new best solutions. Moreover, a natural extension
of such problem to maintenance planning for wind farms is devised for a deterministic case, and then
explored within a simulation approach.
First, a hybridized Memetic Algorithm with a restarting procedure based on a Greedy Randomized
Adaptive Procedure Search (GRASP) was proposed to tackle the VRP with stochastic demands. The
method is proven to be ecient in a classical available benchmark showing better results than current
state-of-the-art methods. During this thesis, the literature review showed a lack of detailed results for
bigger instances involving larger number of customers, as can be encountered in real life applications.
Therefore, we proposed a new data set including 39 instances based on a benchmark originally designed
for the deterministic VRP. The average number of customers in this new set doubles the previous
classical set, and comprises some instances with more than 200 customers. New results were reported
for 29 instances while the remaining ten have been already optimally solved by other authors. The new
benchmark and best solutions achieved by our algorithm enable in the future comparison on middle to
big size instances for which results are available. In addition, new recourse actions and strategies such
as re-optimization can be assessed against the classical recourse policy used in our methods. Currently,
our results can serve as the baseline for evaluating the tradeo between algorithms eciency and the
use of more complex policies to face uncertainties.
Second, a VRP with stochastic travel and service times and hard time windows was introduced since
the majority of related works considered soft time windows, or coupled soft and hard time windows.
This problem also considers continuous random variables with dierent probability distributions which
is fairly uncommon in the related literature. A parallel Memetic Algorithm framework is used to tackle
the problem. Although the base of each thread of the algorithm is based on the same principles, each
one undertakes the uncertainties in a dierent way. Some of the populations are created to solve
deterministic problems for which the uncertain parameters are replaced by the mean values or a
percentile of the considered probability distributions. To the best of our knowledge, this is the rst
129
time a SVRP is solved using such a solution approach. The results conrm a signicant improvement
on the eciency due to the multi-populations parallel algorithm. Moreover, the proposed algorithm
is compared to other works published recently, and the results proved that it is very competitive.
Besides this, it can easily be extended to other SVRPs which make the designed parallel framework a
powerful and exible tool.
The frameworks of the methods designed to solve the previous problems allowed us to state that
good solutions in a deterministic related problem can be used as departure points for good stochastic
solutions. Nevertheless, they require to be further improved by integrating adequately the uncertain-
ties. In our approach, this strategy led to improvements on the algorithms performances. In particular,
it increased the probability of nding near to best known solutions in shorter running times.
In the wind farm turbine maintenance context, a multi-objective optimization problem dealing
with scheduling of maintenance resources is studied. The two objectives taken into account in our
problem are the costs and energy production. A bi-criteria mixed integer program is proposed and
solved within an epsilon constraint method. To the best of our knowledge, this is the rst time
that this kind of approaches is conducted in the wind farm operational decision level context. The
computational results suggest that with our formulation small size instances can be solved by providing
the exact Pareto-front. Nevertheless, they also show that augmentation of the costs, mainly driven by
overtime, can signicantly aect the amount of produced energy. Therefore, we extended this work to
evaluate how these objectives (costs and energy production) behave through the life cycle of a wind
farm project under dierent maintenance strategies. To do so, as is common in the literature, we
devised a Monte Carlo simulation approach based on discrete events simulations. The appearance
of failures and the time required to carry out maintenance activities are considered as stochastic.
Unlike what is usually used in other works, we derived a policy to schedule maintenance tasks in short
term horizon, considering the wind speed for the subsequent days as well as the energy production.
This allows to emulate good solutions to the operational scheduling problem. The results show an
important eect in the life cycle maintenance costs and produced energy. Although other works have
employed simulation to compare maintenance strategies, most of them rely on assigning the resources
to execute the maintenance tasks as fast as they are available. Further research must be devoted to
the introduction of more information to schedule the resources, such as the system status, components
history of failures, etc.
Further work on both SVRPs and wind farm maintenance planning should focus on the independ-
ence assumption of the random variables. Concerning stochastic demands, the interdependent scenario
can arise when demands of close geographical assets are aected by localized events. Moreover, one
can expect that variables representing travel times are highly correlated due to factors such as trac
jams. Similarly, in the wind farms context, the degradation processes of the turbines components are
usually assumed to be independent. This assumption is rather questionable.
Other interesting topics of research are related to the evaluation of the distributions used to model
uncertain parameters. For example, the widespread devices carrying Geographical Positions Systems
(GPS) can be used to conduct several analysis of travel times in dierent areas. This will allow to
evaluate the accuracy and pertinence of using some distributions. In this sense, more experimental
studies are needed to conrm the importance of SVRP solution in real applications. In theoretical
works the Value of the Stochastic Solution (VSS) has proven to be considerable. Our studies showed
that the VSS increases with the size of the problems and also when they are more constrained.
We expect to see an increase in the number of metaheuristic approaches to deal with SVRPs.
The experience of deterministic combinatorial problems must be capitalized and benet to stochastic
variants. This must be accompanied with new strategies to incorporate the parameters variability.
Currently, one of the main problems in such methods remains the time overload generated by prob-
ability calculation to asses either a recourse action or a probabilistic constraint. This is particularly
important in local search procedures which are recognized as time-consuming components. Besides,
130
CHAPTER 6. CONCLUSIONS
branch-and-price methods can be used to increment the amount of exact methods on SVRPs. Ap-
proximations of the recourse costs based on sampling procedures distributions (simulation) can be
used to solve the pricing problems. Additionally, more recourse actions are expected to appear but
they will likely be more complex.
To enable comparisons among dierent recourses, standardized benchmarks are needed to be set
so that fair competition should contribute to improve the results and highlight new properties on
these problems. Finally, although it was not the focus of this dissertation we expect to see an increase
in the number of articles dealing with robust optimization as an important methodology to handle
uncertainties when the parameters probability distributions are unknown. The trends of the literature
on SVRPs and Maintenance Planning make us believe that it will continue to increase in the following
years. We hope that the contributions of this thesis and the research clues identied through it will
help future developments.
131
132
Appendices
133
134
Annexe A
Résumé en français
Les activités de transport jouent un rôle très important dans l'économique. En eet, la logistique et
les activités de transport ont généré 8% du Produit Intérieur Brut aux États-Unis pendant l'année
2015. Le transport rien qu'à lui même peut représenter jusqu'à 60% des coûts logistiques, ce qui le
rend un sujet d'étude important. Sur le plan académique, la communauté de la Recherche Opération-
nelle a largement contribué à la résolution de problèmes soulevés dans le domaine du transport, et en
particulier les problèmes de tournées de véhicules (VRP en anglais pour Vehicle Routing Problem).
Ceci a donné lieu à un grand nombre de publications depuis l'introduction du VRP en 1954. Toute-
fois, la plupart des travaux continuent à considérer que les informations, et donc les paramètres des
problèmes sont connus à l'avance. Cette supposition est rarement vraie dans la réalité, en eet il existe
plusieurs facteurs qui peuvent remettre en cause cette supposition sur la certitude des paramètres.
Dans le contexte du transport urbain par exemple, les temps de trajets peuvent être aectés par les
embouteillages. Les temps nécessaires pour servir les clients peuvent aussi dépendre de la complexité
des services à eectuer, etc.
Les problèmes de type VRP avec incertitudes ont sucité un intérêt grandisant ces dernières années.
Gendreau et al. [13] ont proposé récemment une des revues les plus complètes sur le sujet, démontrant
une activité de recherche croissante. Dans cet article, les auteurs se sont concentré sur les diérents
modèles de programmation stochastique dédiés aux problèmes de tournées avec incertitudes. Pour
analyser et classier les travaux, deux caractéristiques principales sont en général considérées : le type
de paradigme de résolution considéré et les paramètres entâchés par les incertitudes (demandes, pré-
sence de clients, temps...etc.). Les paradigmes de résolution se divisent en deux catégories : l'approche
d'optimisation à priori et la réoptimisation. L'optimisation à priori est liée au cas statique dans le-
quel des décisions doivent être prises ici et maintenant bien avant la révélation des réalisations des
paramètres stochastiques. C'est le paradigme préféré quand les incertitudes peuvent avoir des consé-
quences importantes sur la solution, et donc il est préférable de les anticiper. Parmi ces paradigmes
à priori, nous citons les problèmes avec recours (SPR en anglais pour Stochastic Programming with
Recourse) et les problèmes avec contraintes probabilistes (CCP en anglais pour Chance Constraint
Programming).
Les modèles type SPR utilisent des actions appelées recours qui permettent de réagir face aux
situations de violation de contraintes (échecs) suite à la révélation des paramètres incertains. Les
modèles CCP quant à eux visent à limiter la probabilité de violation des contraintes. Les deux modèles
présentent cependant l'inconvénient d'êtres très lourd à résoudre en termes du temps nécessaire pour
le calcul des coûts relatifs aux actions de recours, et des probabilités de satisfaction des contraintes.
Ainsi, il est crucial de développer des méthodes de résolution qui permettent de gérer les incertitudes
d'une façon ecace et ceci aussi bien pour les modèles SPR que CCP.
Cette thèse utilise la programmation stochastique et le paradigme à priori pour étudier deux
VRP stochastiques et un problème de planication de la maintenance de parcs d'éoliennes. Dans
135
A.1. INTRODUCTION AUX VRP
le chapitre 2 une introduction générale aux problèmes VRPs et une revue des VRPs stochastiques
sont présentées. Le chapitre 3 est consacré au VRP avec demandes stochastiques (VRPSD). Pour
ce dernier une métaheuristique hybride composée d'un algorithme mémétique et d'une procédure de
redémarrage, par une méthode de type Greedy Randomized Adaptive Search Procedure (GRASP), est
proposée. Les résultats de cette approche hybride montrent son ecacité en comparaison avec d'autres
méthodes publiées dans la littérature. De plus un nouvel ensemble d'instances de grande taille, inspiré
d'un benchmark dédié au cas déterministe, est proposé pour servir de base à des comparaisons futures.
Le chapitre 4 se concentre sur le VRP avec temps de trajets et de services stochastiques et fenêtres
de temps dures. Le modèle proposé intégre l'impact de la violation des fenêtres de temps sous forme
de recours, mais impose de garantir des niveaux de services. En outre, pour estimer les temps d'arrivée
chez les clients, une approximation par une loi Log-normale est proposée et démontrée ecace par des
tests statistiques. Pour résoudre le problème, un algorithme méméthique parrallèle et à populations
multiples a été développé. Cette méthode a permis d'obtenir de très bons résultats en comparaison
avec ceux disponibles dans la littérature.
Le chapitre 5 présente une revue de la littérature consacrée à la planication des activités de main-
tenance pour un parc d'éoliennes abordé du point de vue décision opérationnelle. Dans ce problème
nous considérons deux critères de décision : l'opérateur du parc veut minimiser les coûts de mainte-
nance tandis que l'investisseur du parc veut produire la plus grande quantité d'énergie possible. Un
modèle mathématique bi-objectif est utilisé pour modéliser le problème sous forme d'un programme
linéaire à variables mixtes. Ce dernier est ensuite résolu par une méthode de type epsilon-contraintes.
Dans la seconde partie de ce chapitre, le problème précédent est étendu sur un horizon de planication
long tout en considérant les stratégies de maintenance. Ce dernier problème est étudiée et abordée
par une méthode basée sur la simulation. Les resultats obtenus montrent l'impact du choix des règles
de priorité utilisées pour l'ordonnancement des tâches de maintenance sur les coûts et la quantité
d'énergie produite. La thèse se cloture avec le chapitre 6 en orant des pistes pour des recherches
futures sur l'ensemble des problèmes étudiés.
A.1 Introduction aux VRP
Dans le chapitre 2 une introduction générale aux problèmes de tournées des véhicules (VRP en anglais)
est présenté ainsi qu'une étude plus détaillée des problèmes de type VRP avec incertitudes. Les VRPs
ont été largement étudiés depuis leur introduction par Dantzig et Ramser [7]. Ceci peut être expliqué
par deux raisons : l'importance du transport dans les activités humaines (distribution des produits et
services), et le fait que le domaine a été l'origine du développement de diérentes méthodes, exactes
et approchées, pour la résolution de problèmes combinatoires.
Dans sa version de base, le VRP avec contraintes de capacité (CVRP) a comme objectif de
construire un ensemble des tournées de coût minimal qui respectent les contraintes de capacité des
véhicules. Le problème est déni sur un graphe complet non orienté G = (V,E). L'ensemble de n÷uds
est noté V = 0, 1, . . . , i, . . . , n et l'ensemble d'arêtes est E = [i, j]∀i, j ∈ V | i < j. Le n÷ud 0
est associé à un sommet particulier appelé dépôt, et le reste des n÷uds V c = V \ 0 représentent lesclients. De plus, un ensemble de véhicules ayant la même capacité Q sont disponibles au dépôt. Par
ailleurs, chaque arête dans E est associée à un coût non négatif cij , et chaque client dans V c est asso-
cié à une demande qi. La solution du problème est un ensemble de tournées visitant une et une seule
fois chaque client. Chaque tournée est une séquence ordonnée de n÷uds r = r0 = 0, r1 = 0, . . . , rj ,
. . . , rk, rk+1 = 0 qui démarre et nit au dépôt. Ainsi, le coût d'une tournée particulière r est calculé
par Cr =∑kj=0 crj ,rj+1 .
Beaucoup de travaux sont consacrés au CVRP, cependant l'existence de plusieurs cas particuliers
dans la réalité a donné naissance à de très nombreuses variantes. Le lecteur peut se référer au livre
de Toth et Vigo [25] pour une revue de la littérature sur les variantes du CVRP. Une de ces variante
136
ANNEXE A. RÉSUMÉ EN FRANÇAIS
est le VRP avec fenêtres de temps (VRPTW en anglais). Le VRPTW généralise le CVRP par l'ajout
des durées de trajet et de service, ainsi que par la présence de fenêtres de temps [ai, bi] sur le début
de service pour tout n÷ud ∀i ∈ V . Comme le CVRP, le VRPTW peut être dénit sur le graphe G. Le
VRPTW rajoute un temps de trajet tij ∀ i, j ∈ V pour chaque arête et un temps de service ti ∀ V cpour chaque client. Les fenêtres de temps sont classées en deux types : dures et souples [8]. La version
avec fenêtres de temps dures considère le cas où les services chez les clients doivent impérativement
commencer à l'intérieur de la fenêtre de temps. Par conséquent, si un véhicule arrive chez le client
avant l'ouverture de la fenêtre de temps, il doit attendre jusqu'à ce moment-là. Dans le cas où le
véhicule arrive après la fermeture de la fenêtre de temps, aucun service ne peut être eectué. Dans
le VRPTW avec fenêtres souples, les services en dehors des fenêtres sont autorisés mais une pénalité
proportionnelle à l'écart entre la date de début de service et la borne de la fenêtre de temps est souvent
considérée pour ces évènements. La fenêtre de temps pour le dépôt pose une contrainte sur la date de
départ et de retour à ce dernier.
Solution approaches for VRPs
Exact methods
Heuristics Metaheuristics
Constructive
Two-phase
Branch and Price
Branch and Price and Cut
Branch and Cut
Tabu Search
Simulated Annealing
Iterated Local Search
Matheuristics
Clarke & Wright
Nearest Neighborhood
Ant Colony Optimization
Genetic Algorithms
Cluster-first, route second
Route-first, cluster second
Approximate methods
Figure A.1 : Classication des méthodes de résolution pour les VRP.
Pour résoudre le VRPTW (et en général les VRP) beaucoup de méthodes ont été proposées. En
eet, résoudre les VRPs n'est pas une tâche facile car cette catégorie de problèmes fait partie des
problèmes NP-Diciles. Par conséquent, il n'existe pas d'algorithmes de complexité polynomiale qui
peut les résoudre pour toute taille de problème. Les diérentes approches utilisées pour obtenir des
solutions peuvent être classiés en méthodes exactes et méthodes approchées comme le montre la
gure A.1. Les méthodes exactes permettent de trouver la solution optimale, cependant leurs temps
d'exécution sont très élevés à partir d'une taille donnée. Ceci est d'ailleurs le principal inconvénient de
ce type de méthodes. Actuellement, le CVRP est résolu pour des instances allant jusqu'à 200 n÷uds,
alors que ce chire diminue à 100 n÷uds pour le VRPTW. Les méthodes approchées essaient de
trouver un compromis entre la qualité de la solution et le temps d'exécution. Néanmoins, la plupart
de ces méthodes n'orent aucune garantie d'optimalité de la solution obtenue. Parmi les méthodes
approchées, les métaheuristiques sont très utilisées car elles donnent des solutions souvent assez proches
de l'optimum.
La littérature dédiée aux VRPs continue de s'accroitre mais une grande partie des travaux suppose
que les paramètres des problèmes sont connus à l'avance ou d'une façon déterministe [20]. Cependant,
dans la réalité certains paramètres ne peuvent pas être connus avec certitude à cause des conditions
météorologiques, les accidents, de la présence ou non de la clientèle, etc. Les travaux publiés dans
la littérature montrent qu'ignorer les incertitudes conduit à des solutions infaisables et couteuses. Le
137
A.1. INTRODUCTION AUX VRP
tableau A.1 présente une classication des VRPs, telle que suggérée par Pillac et al. [20] selon la
qualité et l'évolution de l'information.
Table A.1 : Taxonomie des VRPs basée sur l'article de Pillac et al. [20]Qualité de l'informationDonnées déterministes Données incertaines
Évolution del'information
Données connuesà l'avance
Statiques et déterministes Statiques et incertaines
Donnéesdynamiques
Dynamiques etdéterministes
Dynamiques et incertaines
Dans le cas statique et déterministe, les paramètres sont considérés comme connus dès la plani-
cation de la solution. Dans le cas déterministe et dynamique les paramètres (ou une partie d'entre
eux) sont complètement inconnus et sont seulement révélés à des moments spéciques. Le cas avec
incertitudes partage une caractéristique avec le cas dynamique, étant donné que les vraies valeurs
des paramètres ne deviennent connues qu'à des moments précis. Dans le cas statique et incertain, on
dispose d' informations exploitables sur l'incertitude des paramètres (leurs lois de probabilité, les inter-
valles dont lesquels ils prennent leurs valeurs, etc.). Ces informations sont donc utilisées pour résoudre
le problème. Finalement dans le cas dynamique et incertain, les paramètres (ou une partie d'entre
eux) sont inconnus, mais comme dans le cas statique et incertain, il existe des informations relatives
à ces derniers. Une autre diérence fondamentale entre les problèmes statiques et dynamiques est la
façon dont laquelle les solutions sont calculées. Dans le cas statique, une solution reste non modiable
quelques soient les vraies valeurs des paramètres. Dans le cas dynamique en revanche, la solution peut
être constamment modiée pour s'adapter aux informations qui arrivent au fur et à mesure.
Trois approches ont été principalement utilisées pour modéliser les incertitudes des paramètres
des VRPs, à savoir : la programmation stochastique, l'optimisation par intervalles, et la logique oue.
L'optimisation par intervalles modélise les paramètres incertains par des intervalles de valeurs pos-
sibles. L'objectif poursuivi par l'optimisation par intervalles est de trouver des solutions qui sont
faisables pour toutes les réalisations possibles des paramètres [2]. Des VRPs avec incertitudes sur les
demandes et les temps apparaissent dans la littérature. Adulyasak et Jaillet [1] traitent la version avec
temps de trajets incertains. Les auteurs proposent une comparaison entre l'optimisation stochastique
et l'optimisation par intervalle. Les auteurs montrent que les solutions robustes surpassent largement
les solutions issues de l'optimisation stochastique dans le cas où les paramètres ne sont pas modélisés
par la bonne loi de probabilité. Enn, la logique oue permet aussi de représenter les incertitudes en
utilisant des variables oues. Certains travaux combinent deux approches en utilisant par example des
lois de probabilité pour modéliser les paramètres du problème et des nombres ous pour modéliser
leur espérance ou leur variance [15].
Parmi les trois approches, la programmation stochastique est la plus répandue pour résoudre
les VRPs avec incertitudes. Dans cette approche, les paramètres sont modélisés par des variables
aléatoires de lois connues. De ce fait, les contraintes des problèmes peuvent ne plus être respectées
par les solutions. Par exemple, dans le VRP avec demandes stochastiques (VRPSD), la réalisation
de la demande d'un client peut dépasser la capacité restante du véhicule. Si une contrainte n'est pas
respectée par la réalisation des parametres du problème un "`échec"' est dire de se produire. Pour les
considérer, deux types des modèles sont utilisés [12] : la réoptimisation et les approches à priori avec
recours.
Les modèles qui utilisent la réoptimisation ne reposent pas sur une solution xe. En eet la solution
est construite et modiée au fur et à mesure que les informations apparaissent. Dès révélation de ces
dernières, les tournées peuvent-être réoptimisées à nouveau. Toutefois, ce type d'approche rend la
coordination des véhicules assez ardue et la vitesse à laquelle les solutions nécessitent d'être fournies
reste un problème.
138
ANNEXE A. RÉSUMÉ EN FRANÇAIS
Les méthodes du type à priori sont basées sur des solutions statiques et peuvent être divisées en
deux catégories : les problèmes avec recours (SPR) et les problèmes avec contraintes probabilistes
(CCP). Les premiers utilisent des actions appelées "`recours"' qui permettent de rétablir la faisabilité
de la solution quand des échecs se produisent. Un exemple pour le VRPSD est de revenir au dépôt
quand la demande d'un client dépasse la capacité disponible du véhicule. Après le passage au dépôt,
le véhicule reprend la tournée depuis le client où l'échec s'est produit. En outre, le véhicule complète
la demande restante avant de servir les clients suivants planiés dans la tournée. Les coûts associés
à ce type d'actions sont rajoutés dans la fonction-objectif du problème. D'autre part, les problèmes
avec des contraintes probabilistes cherchent à limiter la probabilité des échecs à un seuil. Les CCP
sont recommandés quand la dénition du recours est trop dicile ou quand un niveau de service doit
être garanti.
Une façon commune de classier les VRP stochastiques (SVRP) consiste à considérer les paramètres
entâchés par les incertitudes. Selon Gendreau et al. [13] trois catégories peuvent être considérées :
les VRPs avec demandes stochastiques (VRPSD pour VRP with Stochastic Demands), VRPs avec
incertitudes sur la présence des clients (VRPSC), et les VRPs avec temps stochastiques (VRPST).
Le problème avec demandes stochastiques a été le plus étudié et les modèles avec recours ont été
privilégiés par apport aux modèles avec contraintes probabilistes. Le recours classique considère que
lorsque la capacité d'un véhicule est épuisée, le véhicule fait un retour au dépôt pour s'approvisionner
puis revient chez le client où l'échec s'est produit. D'autres recours existent pour le VRP avec la
même politique de réapprovisionnement mais des visites au dépôt pouvant être eectuées avant que la
capacité du véhicule ne soit atteinte. De cette façon, des économies en temps et en distance parcourue
peuvent être réalisées. Ils existent d'autres recours plus complexes toutefois les recours simples ont été
favorisés. Pour résoudre le VRPSD des méthodes exactes ont été proposés pour les cas où des recours
simples sont utilisés. Cependant c'est les mpethodes apporchées les plus utilisés. Ces méthodes sont
souvent testés sur un ensemble standard d'instances, toutefois la taille de ces dernières demeurent
petites.
Les problèmes avec clientes stochastiques sont ceux dans lesquels la présence des clients est incer-
taine. Quand le véhicule arrive chez le client, ce dernier peut-être présent ou non avec une certaine
probabilité. Les VRPCS sont les moins étudiés parmi les VRP stochastiques. En eet, les VRP avec
temps (trajet ou service) stochastiques (VRPST) ont reçu plus d'attention que les VRPCS mais de-
meurent moins étudiés que les VRPSD. La plupart des travaux consacrés à cette catégorie de problèmes
considèrent des fenêtres de temps sur le service. Ces fenêtres de temps peuvent être souples ou
dures , les premières étant largement favorisées. Ceci peut être expliqué par l'eet des fenêtres de
temps dures sur les temps d'arrivée chez les clients. Généralement, les temps de trajets sont représentés
par des lois qui ont des propriétés de convolution, mais les fenêtres dures empêchent l'utilisation de
ces propriétés. Des problèmes qui prennent en compte les deux types des fenêtres sont les plus étudiés.
Ces travaux considèrent que la date au plus tôt de service (début de fenêtre de temps) doit absolu-
ment être respectée mais autorise le service après la date de fermeture des fenêtres. La complexité des
VRPST explique le faible nombre de publications utilisant des méthodes exactes. L'utilisation de ces
dernières est limitée aux cas dans lesquels les variables aléatoires sont discrètes, ou quand l'espace de
scénarios réduit, où au cas de variables aléatoires additives (avec des convolutions possibles à calculer).
De même que pour les autres VRP stochastiques, les méthodes approchées restent les plus privilégiées
pour résoudre le VRP avec temps stochastiques.
La complexité des VRP avec incertitudes a limité le développement des recherches sur ce sujet ainsi
que la taille des instances résolues. Toutefois, comme pour le cas déterministe, il existe un réel besoin
de méthodes puissantes pour résoudre les problèmes dans des conditions réelles, autant en termes
de taille, de nature de paramètres aléatoires et de contraintes complexes. Donc, dans cette thèse on
propose d'étudier les VRP de nature stochastique et de développer des approches adaptées capables
de résoudres des instances de grande taille. Ce travail est l'un des rares à considérer des fenêtres de
139
A.2. UNE MÉTHODE HYBRIDE POUR LES VRP AVEC DEMANDES STOCHASTIQUES
temps dures sur le service tout ayant des temps de trajet et service stochastiques.
A.2 Une méthode hybride pour les VRP avec demandes sto-
chastiques
Le chapitre 3 présente une méthode de résolution approchée pour le VRP avec demandes stochastiques
(VRPSD). Un nouvel ensemble d'instances est proposé pour permettre des comparaisons futures. La
méthode de résolution développée pour ce problème est un algorithme mémétique (MA) hybridé
avec une méthode gloutonne du type Greedy Randomized Adaptive Search Procedure (GRASP).
L'algorithme proposé montre une meilleure performance que les méthodes trouvées dans la littérature.
En eet, pour les instances "`classiques"' de Christiansen et Lysgaard [4] notre méthode, trouve toutes
les meilleures solutions connues dans un temps de calcul réduit.
Le VRPSD est une généralisation du CVRP dans lequel les demandes des clients sont représentées
par des variables aléatoires. Le problème étudié dans ce chapitre considère une version du VRPSD
avec les caractéristiques suivantes. La demande qi de chaque client i est modélisée par une variable
aléatoire qui suit une loi de probabilité ψ avec espérance E [qi] > 0 et variance V ar [qi] > 0. Il est
supposé que la loi ψ est connue et que les demandes sont indépendantes entre elles. De plus, il est
considéré que la loi de probabilité de la somme des variables ψ est aussi une variable aléatoire de
même nature ψ.
E [Cr] =
k∑j=0
crjrj+1 +
k∑j=1
ERCrj (A.1)
Pour modéliser le VRPSD nous avons utilisé la programmation stochastique avec recours. Le
recours utilisé est le même que celui proposé par Bertsimas [3]. Celui-ci prévoit que lorsqu'un véhicule
atteint sa capacité Q, il retourne au dépôt pour déchargement. Ensuite, le véhicule reprend sa tournée
depuis le client où la rupture de charge est survenue pour compléter la demande qui n'a pas pu être
satisfaite, puis le véhicule continue avec sa tournée. L'objectif du VRPSD avec recours est de minimiser
le côut total des tournées y compris ceux relatifs aux recours. L'évaluation du coût d'une tournée est
donnée par l'équation A.1. Elle inclut les coûts déterministes et la valeur moyenne du recours (ERC).
Pour cette dernière, la demande cumulée jusqu'au client ri est dénie par Dri =∑ij=1 qrj . Du fait de
l'hypothèse d'independence des variables aléatoires relatives aux demandes, l'espérance et la variance
de la demande cumulée se calcule par E [Dri ] =∑ij=1E [qrl ], V ar [Dri ] =
∑ij=1 V ar [qrl ]. La valeur
moyenne du recours est donc calculée par l'équation A.2. Il faut remarquer que la otte est supposée
illimitée, et que le nombre de véhicules n'a pas d'impact sur la fonction objectif. Ainsi, pour éviter des
solutions avec des tournées avec des échecs fréquents, la contrainte E [Drk ] < Q ∀r est aussi rajoutée.
ERCri = 2 · c0ri ·
[ ∞∑u=1
(P(Dri−1
≤ uQ)− P (Dri ≤ uQ)
)](A.2)
La méthode de résolution proposée pour résoudre le VRPSD est une hybridation entre un algo-
rithme mémétique (MA) [21] et une méthode de type Greedy Randomized Adaptive Search Procedure
(GRASP) [10]. L'objectif de la méthode baptisé MA+GRASP est de trouver des solutions de qualité
en peu de temps même pour des grandes instances. Pour échapper aux optima locaux, le MA utilise
une méthode de re-démarrage basé sur la méthode GRASP. Le but de cette procédure est d'accroître
l'exploration de l'espace de recherche tout en controlant son impact négatif sur les temps d'exécution.
L'algorithme 6 présente un aperçu global du fonctionnement de la méthode. Elle utilise une po-
pulation Pop à taille xe dans laquelle chaque individu représente une solution du problème. L'algo-
rithme exécute les lignes 4 et 19 jusqu'à ce que un critère d'arrêt soit satisfait. À chaque itération,
MA+GRASP crée une nouvelle solution en croisant deux solutions présentes dans la population. Ainsi,
140
ANNEXE A. RÉSUMÉ EN FRANÇAIS
Algorithm 6 MA + GRASP1: Pop← Initialize population2: φ← Constant > 03: i← 14: while not (stop) do5: c← crossover (Pop)6: Mutation, pmp (c)7: Split (c)8: Local Search, pls (c)9: if Executed Local Search then10: Concatenate and Split (c)11: end if12: if Is Not Clone (c) then13: Update Population (c,Pop)14: end if15: Update(i)16: if i ≥ φ then17: Restart with GRASP (Pop)18: i← 119: end if20: end while
cette nouvelle solution peut être modiée par des procédures de mutation et de recherche locale (lignes
6 et 8). Comme MA+GRASP est basé sur un algorithme génétique, des méthodes permettant de co-
der et de décoder une solution sont utilisés. Le décodage utilise la méthode Split de Prins [21], alors
que le codage concatène toutes les tournées de la solution sans considérer le dépôt. Dans la ligne 17,
et si le nombre d'itérations sans amélioration de la meilleure solution trouvée dépasse une valeur φ,
l'algorithme redémarre à nouveau le MA. Cette action implique la réinitialisation de la population de
MA+GRASP.
Algorithm 7 GRASP RestartRequire: Population Pop, iterations1: BestIndividual← Pop[0]
2: NumRuns←⌈(Popsize−1)
2
⌉3: Clear Population(Pop)4: i← 15: while i ≤ NumRuns do6: j← 17: BestCost←∞8: BestSol← null9: while j ≤ iterations do10: c← Generate Individual with RNN11: Split (c)12: Local Search (c) . Only deterministic costs13: if Cost (c) < BestCost then14: BestCost← Cost (c)15: BestSol← c16: end if17: j ← j + 118: end while19: Local Search (BestSol)20: Add to population (BestSol,Pop)21: i← i+ 122: end while23: Add to population (BestIndividual,Pop)24: Complete Population with Random Individuals25: Sort(Pop)
Pour le redémarrage, MA+GRASP utilise une méthode GRASP qui se charge de remplacer l'an-
cienne population. Cette méthode exécute la plupart de temps une recherche locale qui considère juste
le coût déterministe. De cette manière, les temps d'exécution sont contrôlés. L'idée derrière cette façon
141
A.2. UNE MÉTHODE HYBRIDE POUR LES VRP AVEC DEMANDES STOCHASTIQUES
de procéder est que, même si les solutions déterministes n'ont pas la meilleure performance dans le
contexte stochastique, elles peuvent servir de base pour être améliorées rapidement. Alors, une fois
que des bonnes solutions déterministes ont été trouvées, elles sont améliorées avec la recherche locale
qui prend en compte les coûts du recours. Il faut remarquer qu'une partie de la population est com-
plétée par des solutions aléatoires pendant le redémarrage. Cette partie a comme but d'augmenter la
diversité et donc l'exploration de l'espace de recherche.
Notre approche MA+GRASP permet d'obtenir de très bons résultats. Une comparaison avec les
travaux de Mendoza et al. [17] basé sur un GRASP, et ceux de Goodson et al. [14] ( présentant un récuit
simulé) conrme la supériorité de notre méthode. MA+GRASP arrive à trouver toutes les meilleures
solution connues (BKS en anglais). De plus, elle présente un écart moyen de juste 0.004%. Cela veut
dire que l'algorithme retrouve des solutions très proches du BKS à chaque exécution et pour chaque
instance (10 exécutions par instance). De plus, MA+GRASP utilise moins de dix secondes pour trouver
ces valeurs, ce qui permet de montrer que MA+GRASP est très ecace pour résoudre le VRPSD.
Pour tester la méthode sur des instances plus diciles, on a proposé un nouvel ensemble d'instances
basé sur les ensembles A et P d'Augerat qui n'ont pas été considérés par Christiansen and Lysgaard
[4] (22 instances), ainsi que des chiers test de Cristodes, Mingozi et Toth CMT (4 instances), et de
Rochat et Taillard (13 instances) 1. Le nouvel ensemble présente en moyenne 2.4 plus de clients que
l'ensemble original de Christiansen and Lysgaard. Les caractéristiques et les BKS du nouvel ensemble
sont reportées dans le tableau A.2.
On constate comme illustré dans le tableau A.2 que le coût des solutions BKS est principalement
composé du coût déterministe (Det) sur les instances testées. Cela explique pourquoi les solutions qui
considèrent juste le coût derministent sont de bonnes solutions aussi pour le problème stochastique.
Cependant, si seuls les meilleurs solutions du problèmes déterministe (BDS) sont évaluées, une forte
augmentation intervient dans les coûts du recours lorsque ce dernier est considéré. On peut également
clairement noter que pour des instances de plus grande, les économies générées par les solutions
stochastiques sont plus importantes.
Finalement, pour analyser l'impact de la méthode GRASP sur les perfomances de la méthode
hybride MA+GRASP, un ensemble de tests numériques a été eectué. Deux versions modiées de
MA+GRASP ont été utilisées pour résoudre le VRPSD pour le nouvel ensemble d'instances. La pre-
mière considère le redémarrage mais, ne fait pas appel au GRASP (MA+RANDOM). Dans ce dernier
algorithme, tous les individus crées pour remplacer l'ancienne population sont générés aléatoirement.
La deuxième variante est une version sans redémarrage (NR-MA). Une série de calculs eectuée sur les
trois algorithmes a été réalisée, le but étant d'analyser le temps que chacun prend pour atteindre les
valeurs cibles pour chaque instance. Cet ensemble de données a servis pour construire des graphiques
de probabilités cumulés comme proposé par Reyes and Ribeiro [22] (graphiques MTT). Les gures
A.2 et A.3 montrent les graphiques pour diérentes valeurs cibles en fonction de l'écart à la meilleure
solution connue. Il est évident que la méthode MA+GRASP a l'avantage quand il s'agit de trouver
des solutions proches du BKS. En eet, MA+GRASP a plus de chance de trouver tous les BKS avec
une même quantité de temps en comparaison avec le MA+RANDOM ou NR-MA. Donc, ceci conrme
que le redémarrage avec la méthode GRASP, qui utilise de "`bonnes"' solutions déterministes comme
base pour le contexte incertain, génère un eet positif sur les résultats trouvés par la méthode.
On peut conclure par les résultats obtenus que MA+GRASP a une performance supérieure à celle
des autres méthodes de la littérature. En comparaison avec les versions sans redémarrage ou avec
redémarrage complètement aléatoire, le MA+GRASP retrouve des solutions de meilleure qualité en
moins de temps. Dans la continuité de ce travail, on peut s'intéresser à l'utilisation de nouveaux
recours, à la considération d'autres paramètres stochastiques (comme le temps), et l'extensions du
problème pour considérer des demandes corrélées.
1Toutes les instances sont disponibles dans la page : http ://vrp.atd-lab.inf.puc-rio.br
142
ANNEXE A. RÉSUMÉ EN FRANÇAIS
Table A.2 : Synthèse des résultats sur le nouvel ensemble d'instancesBKS BDS
Instance |V | Min veh Q FC Veh Total Det Rec Total Rec VSSA-n61-k9 61 9 100 0.98 10 1144.23 1084 60.23 1215.37 181.37 5.85%A-n62-k8 62 8 100 0.92 9 1430.81 1375 55.81 1533.07 245.07 6.67%A-n63-k10 63 10 100 0.93 11 1459.49 1412 47.49 1581.17 267.17 7.70%A-n63-k9 63 9 100 0.97 10 1847.69 1734 113.69 1991.75 375.75 7.23%A-n64-k9 64 9 100 0.94 10 1569.85 1534 35.85 1699.68 298.68 7.64%A-n65-k9 65 9 100 0.97 10 1313.30 1266 47.30 1421.52 247.52 7.61%A-n69-k9 69 9 100 0.94 10 1259.35 1214 45.35 1339.55 180.55 5.99%A-n80-k10 80 10 100 0.94 11 1987.17 1918 69.17 2109.56 346.56 5.80%E-n23-k3 23 3 4500 0.75 3 569.72 569 0.72 569.72 0.72 0.00%E-n30-k3 30 3 4500 0.94 4 504.55 503 1.55 569.92 32.92 11.47%E-n76-k10 76 10 140 0.97 11 885.11 861 24.11 911.70 81.70 2.92%E-n76-k14 76 14 100 0.97 16 1118.90 1086 32.90 1187.18 166.18 5.75%E-n76-k7 76 7 220 0.89 7 698.95 692 6.95 723.96 41.96 3.46%E-n76-k8 76 8 180 0.95 8 771.23 744 27.23 791.88 56.88 2.61%E-n101-k14 101 14 112 0.93 15 1164.149 1116 48.15 1233.84 166.84 5.65%E-n101-k8 101 8 200 0.91 8 839.47 824 15.47 878.36 63.36 4.43%P-n55-k8 55 7 160 0.93 7 607.71 581 26.71 631.82 43.82 3.82%P-n65-k10 65 10 130 0.94 10 854.06 802 52.06 861.52 69.52 0.87%P-n70-k10 70 10 135 0.97 11 882.01 851 31.01 929.82 102.82 5.14%P-n76-k4 76 4 350 0.97 4 609.54 593 16.54 609.62 16.62 0.01%P-n76-k5 76 5 280 0.97 5 648.11 628 20.11 649.10 22.10 0.15%P-n101-k4 101 4 400 0.91 4 686.81 684 2.81 694.47 13.47 1.10%CMT12 101 10 200 0.91 10 982.80 827 155.81 984.31 164.31 0.15%CMT11 121 7 200 0.98 8 1201.15 1187 14.15 1226.62 189.62 2.08%CMT4 151 12 200 0.93 12 1072.19 1036 36.19 1130.31 114.31 5.02%CMT5 200 16 200 1.00 18 1378.85 1355 23.85 1486.96 209.96 7.27%Tai75a 76 10 1445 0.95 11 1653.85 1649 4.85 1718.39 102.39 3.76%Tai75b 76 9 1679 0.99 10 1353.09 1343 10.09 1438.62 99.62 5.95%Tai75c 76 9 1122 0.94 9 1349.87 1332 17.87 1396.98 110.98 3.37%Tai75d 76 9 1699 0.93 9 1392.86 1391 1.86 1444.39 87.39 3.57%Tai100a 101 11 1409 0.98 12 2107.71 2089 18.71 2388.31 350.31 11.75%Tai100b 101 11 1842 0.96 12 1985.33 1986 29.33 2118.93 181.93 6.31%Tai100c 101 11 2043 0.93 11 1421.66 1413 8.66 1526.73 126.73 6.88%Tai100d 101 11 1297 0.95 12 1602.53 1599 3.53 1748.94 174.94 8.37%Tai150a 151 15 1544 0.94 15 3211.55 3188 23.55 3574.95 530.95 10.10%Tai150b 151 14 1918 0.95 15 2792.39 2789 3.39 3113.52 397.52 10.31%Tai150c 151 14 2021 0.99 15 2406.13 2380 26.13 2546.29 201.29 5.50%Tai150d 151 14 1874 0.97 15 2718.39 2692 26.39 3044.91 406.91 10.64%Tai385 386 46 65 1.00 55 29364.03 28007 1357.03 31360.45 7008.45 6.22%
0
0,1
0,2
0,3
0,4
0,5
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0,7
0,8
0,9
1
0 10 20 30 40 50 60 70 80
Cu
mu
lati
ve p
rob
abili
ty
Time (seconds)
MTTT Plot - Target Value 5% gap to BKS
MA+GRASP NR - MA MA+RANDOM
0
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MTTT Plot - Target Value 1% gap to BKS
MA+GRASP NR - MA MA+RANDOM
Figure A.2 : grapheique MTT pour les versions de MA - 5% and 1%
0
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MA+GRASP NR - MA MA+RANDOM
0
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Cu
mu
lati
ve p
rob
abili
ty
Time (seconds)
MTTT Plot - Target Value 0% gap to BKS
MA+GRASP NR - MA MA+RANDOM
Figure A.3 : grapheique MTT pour les versions de MA - 0.5% and 0%
143
A.3. UN ALGORITHME PARALLÈLE POUR LES VRP AVEC TEMPS DE TRAJET ETTEMPS DE SERVICE STOCHASTIQUES
Contributions
Des résultats préliminaires dans ce chapitre ont été présentés à la conférence CIE45 :
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2015)
A Memetic Algorithm for the Vehicle Routing Problem with Stochastic Demands
In Proceedings of the 45th International Conference on Computers & Industrial Engineering CIE45
Metz, France, 2830 October, 2015.
Un article complet dédié au VRPSD fait l'objet d'une révision mineure dans le journal Computers
& Operations Research.
A.3 Un algorithme parallèle pour les VRP avec temps de trajet
et temps de service stochastiques
Dans le chapitre 4, on a étudié la version du VRP avec temps de trajet et temps de service stochastiques
dans un contexte des tournées de maintenance. Les fenêtres de temps considérées dans ce travail sont
dures. Le caractère aléatoire des temps de trajets reète les eets des conditions climatiques, l'état
des rues, accidents, etc. Les temps de service stochastiques représentent la variabilité de la durée de
réparation d'une panne. Pour résoudre le problème un algorithme mémétique qui utilise plusieurs
populations, chacune étant utilisée pour résoudre le problème avec des considérations spéciques sur
pour l'intégration des incertitudes. Des informations sont partagées entre les diérentes populations,
dans un paradigme d'algorithme parralèle, par transfert d'individus d'une population vers une autre.
Le VRP avec temps de trajet et de service stochastique et fenêtres de temps (SVRPTW en anglais)
utilise le VRPTW comme base. La diérence est que les temps de service et de trajet sont des variables
aléatoires désignées comme si (∀i ∈ Vc) et tij (∀ (i, j) ∈ E ′). En plus l'utilisation d'un véhicule a un
coût xe M . Un modèle combinant des contraintes probabilistes et recours (CCP + SPR) est utilisé
pour dénir des pénalités quand le service n'est pas eectué dans les fenêtres des clients, tout en
garantissant que ces événements soient rares. La partie CCP considère trois niveaux de service dans
notre modèle. Le premier (α) garantit une probabilité de service pour chaque client, β est utilisé pour
la probabilité de retour des véhicules au dépôt avant la date limite. Le niveau de service γ assure
que tous les clients dans la solution soient servis avec une probabilité γ. En dénissant AT i comme
le temps d'arrivée chez le client i, les conditions précédentes exprimées pour les clients et dépôt sont
décrites par les contraintes (A.3) et (A.4) pour chaque tournée. Ainsi, le niveau de service pour une
solution s est assuré par (A.5) dans laquelle Ur est la probabilité de servir tous les clients de la tournée
r.
P(AT rj ≤ brj
)≥ α ∀rj ∈ Vc (A.3)
P(AT rk+1
≤ b0)≥ β (A.4)∏
r∈sUr ≥ γ (A.5)
Le recours proposé pour le problème a été déjà décrit dans la littérature, cependant il n'existe pas
des résultats pour ce type de recours. Ce dernier considère que si un véhicule arrive chez un client
i ∈ V c plus tard que la date de fermeture de sa fenêtre de temps bi, le véhicule continuera sa route
sans eectuer le service. Un nouveau véhicule viendra eectuer le service plus tard chez ce client. Ce
recours conduit à une pénalité équivalente à l'utilisation d'un véhicule dédiée pour servir le client i
tout seul. Le recours est simple mais, il faut retenir que tous les temps sont stochastiques, donc les
échecs ne peuvent pas être connus à l'avance. La vraie valeur des temps de trajets n'est connue qu'une
fois la traversée du lien est terminée, de même pour le temps de service.
Le coût d'une tournée peut être déni par l'équation (A.6). Trois parties composent le coût de la
144
ANNEXE A. RÉSUMÉ EN FRANÇAIS
420 440 460 480 500 520 540 560
Arrival Time at i (a)
TimeD
ensi
ty
500 510 520 530 540
Start Service Time at i (b)
Time
Den
sity
20 40 60 80 100
Maintenance time (c)
Time
Den
sity
0 20 40 60 80
Travel Time i to j (d)
Time
Den
sity
520 540 560 580 600 620 640 660
Arrival Time at j (e)
Time
Den
sity
Figure A.4 : Temps d'arrivée et de début des services chez un client.
tournée, d'abord la valeur xe liée à l'utilisation d'un véhicule, le coût des arêtes (cij) parcourues dans
la tournée, et le coût moyen du recours. Dans ce chapitre le coût (M) est choisie comme étant très
élevé. À cet eet, l'optimisation du problème est du type hiérarchique, d'abord minimisant le nombre
de tournées, et ensuite les coûts tournée et des recours.
cr = M +
k∑j=0
crjrj+1 +
k∑j=1
P(AT rj > brj
)·(2 · c0,rj +M
)(A.6)
Il faut noter que les temps d'arrivées chez les client sont aussi des variables aléatoires. En eet,
AT j dépend des temps de trajets et de services précédant l'arrivée chez le client j. La gure A.4
expose une telle situation. La partie (4.a) représente la répartition des dates d'arrivée chez un client
i, les lignes verticales représentent la fenêtre de temps. La répartition des dates de début de service
(compte tenu de la contrainte de fenêtre de temps) est tracée dans la gure (A.4.b). Les gures A.4.c
et A.4.d représentent la répartition des temps de service (maintenance) et des temps de trajet entre
i et j. Finalement, la partie A.4.e est le temps d'arrivée chez le client j. La gure illustre la diculté
de modéliser les temps d'arrivée dans les problèmes avec temps stochastiques puisque les contraintes
de fenêtres de temps rendent impossible l'utilisation de la convolution et de ses propriétés.
La loi de probabilité de AT i est nécessaire pour la vérication des contraintes probabilistes et le
calcul de la fonction-objectif. Dans ce travail AT i est aprochée par la variable AT ri de distribution
Log-normale à l'issue d'une étude comparative de plusieurs lois. En utilisant les instances de Solomon
[24], une série de tournées ont été construites avec l'heuristique de Clarke et Wright. Les temps de
trajet ont été simulés en utilisant la loi Log-normale et les temps de service avec la loi Gamma.
Les espérances de ces variables sont égales aux valeurs déterministes des instances originalles, et les
variances ont été calculées en utilisant des coecients de variation de 0.2. Les résultats de cette analyse
montrent que la loi Log-normale présente la meilleure performance parmi plusieurs lois testées. En
eet, les écarts entre les variables AT i et AT ri restent petits même si le temps d'arrivée ne sont pas
statistiquement représentés par des lois log-normales.
L'algorithme proposé pour résoudre le SVRPTW est un algorithme mémétique (MA) avec plusieurs
populations (MPMA Multi Population Memetic Algorithm en anglais). Chaque population fonctionne
comme un algorithme mémétique individuel mais communique des informations aux autres popula-
145
A.3. UN ALGORITHME PARALLÈLE POUR LES VRP AVEC TEMPS DE TRAJET ETTEMPS DE SERVICE STOCHASTIQUES
tions à des moments précis. La structure de base des algorithmes mémétiques est la même que celle
décrite dans l'algorithme 6. Cependant, la gestion de la diversité est controlée par la communication
entre les populations. De plus les MA individuels n'utilisent pas une procédure de redémarrage. La
communication entre les MA se fait grace au transfert des solutions d'un MA vers un autre MA.
Quand une solution est échangée entre deux MA, celui qui la reçoit l'utilise comme s'il s'agissait d'un
nouvel individu créé par la procédure de croissement. Comme les MA fonctionnent en parallèle, la
communication s'exécute d'une manière asynchrone controlée par une mesure de temps. Quand les
diérents MAs se terminent, le MPMA maître prend les meilleures solutions de chaque MA et les
décode avec la simulation Monte Carlo. Cette étape est très importante car la simulation permet
d'estimer AT ri et donc le "`vrai"' coût des tournées ainsi que leur faisabilité.
Pour ce chapitre on dénit trois (K = 3) MA, appelés MA1, MA2 et MA3. En plus, chaque MA
est en charge de résoudre son propre problème. MA2 résout le VRPTW avec les temps de trajet et de
service égaux à leur valeurs moyennes. MA3 est en charge d'un VRPTW mais les valeurs des temps
de trajets et de service sont plus grandes que les valeurs moyennes (choisis comme le percentile 75%
de chaque loi). Le but de MA2 et MA3 est de trouver de bonnes solutions de base pour MA1. Ce
dernier est en charge de résoudre le SVRPTW en utilisant la loi Log-normale comme approximation
de AT ri .
Algorithm 8 MPMARequire: α, β, γ, τ, numSimulations, runT ime, F1: Create(MAf ) ∀f = 1...F2: initialT ime← CurrentTime3: In parallel : // The MAs start and run in parallel4: for all f = 1...F do
5: run(MAf (α, β, γ, τ, runT ime)) // See algorithm 36: end for
7: while CurrentTime - initialT ime ≤ runT ime do8: Communicate MAf ∀f = 1...F9: end while
10: for all f = 1...F do
11: Best← Best ∪Get best Chromosomes(MAf )12: end for
13: Pool← 14: for all C ∈ Best do15: Split(C,α, β, γ, numSimulations) // Using Monte Carlo Simulation16: Pool← Pool ∪ Simulated routes during Split17: end for
18: return SolveSetPartitioning(Pool, γ)
L'algorithme MPMA est testé sur des instances modiées de Solomon [24]. Pour valider l'impor-
tance de la présence des trois populations, diérentes versions de MPMA ont été testées. Ces résultats
sont présentés dans le tableau A.3. Selon les données, on voit que l'utilisation des diérentes MA
réduit le nombre de véhicules cumulés pour les instances de Solomon. De plus, en moyenne le nombre
de véhicules et le coût des tournées est réduit par rapport aux autres versions. Cependant le MPMA
complet (avec 3 populations) présente des coûts de recours plus élevés. On peut en conclure que le
fait d'utiliser plusieurs populations génère un impact positif dans les résultats. Ceci montre que les
méthodes de résolution pour les problèmes stochastiques doivent être adaptés pour améliorer leur
performance.
Bien que les résultats du MPMA sont intéressants, il est nécessaire de le comparer avec d'autres
méthodes publiées dans la littérature. Parmi les problèmes récemment étudiés, les travaux les plus
proches du problème proposé sont ceux de Miranda et Conceição [18], et Nguyen et al. [19]. Les
deux travaux considèrent le SVRPTW avec temps de trajets stochastiques. Miranda et Conceição [18]
prennent également en compte des temps de service stochastiques. Les services après la fermeture des
fenêtres de temps sont autorisés dans les deux travaux. Cela veut dire que les services sont toujours
exécutés par les véhicules même en cas d'arrivées tardives. Les résultats sont donnés sur les instances
146
ANNEXE A. RÉSUMÉ EN FRANÇAIS
Table A.3 : Comparaison des diérentes MPMA Instances avec 100 clients
Metric MA1 MA1 + MA2 MA1 + MA3MA1 + MA2
+ MA3
# Véhicules Meilleuressolutions
579 575 576 554
# Véhicules moyenne 616.7 612.7 608.4 577.3Coût déterministe moyen 1156.56 1158.88 1138.24 1123.87Coût du recours moyen 3.86 3.90 3.88 31.16Temps moyen pendant MA (s) 100 100 100 100Temps moyen simulation (s) 35.28 65.06 61.85 54.70Temps total moyen (s) 135.28 165.06 161.85 154.80
Table A.4 : Comparaison du MPMA avec la métaheuristique ILS de Miranda et Conceição [18]
InstanceMé-thode
# Veh.Moyen
CDMoyen
NS MinMoyen
NSMoyen
ENSMoyen
Max.ENS
Temps
(s)
R105 ILS 17.67 1615.75 81.66% 99.31% 0.18% 1.81% 18.40MPMA 17.30 1470.89 80.55% 97.30% 0.39% 2.81% 31.82
R109 ILS 15.00 1488.81 79.53% 95.45% 0.17% 0.89% 6.36MPMA 15.00 1270.27 80.96% 98.00% 0.28% 3.44% 32.21
C101 ILS 17.00 2284.77 83.82% 95.52% 0.22% 0.87% 29.49MPMA 17.00 1372.27 80.42% 95.42% 0.64% 3.56% 31.77
C106 ILS 14.67 1722.32 81.33% 98.65% 0.18% 0.70% 5.56MPMA 14.00 1141.17 80.57% 96.01% 0.72% 4.78% 32.25
RC101 ILS 19.67 2012.76 78.32% 100.22% 0.16% 1.42% 15.18MPMA 19.60 1841.32 80.98% 97.53% 0.46% 4.98% 31.77
RC106 ILS 14.67 1584.63 82.26% 95.57% 0.16% 0.82% 24.43MPMA 15.30 1534.95 81.07% 97.87% 0.40% 4.64% 32.08
RC107 ILS 14.00 1569.10 83.03% 96.23% 0.16% 0.86% 34.99MPMA 13.70 1368.53 81.46% 98.44% 0.23% 3.01% 32.40
Avg. ILS 16.10 1754.02 81.42% 97.28% 0.18% 1.05% 19.20MPMA 15.99 1428.49 80.86% 97.20% 0.44% 3.89% 32.04
classiques de Solomon [24] dédiées au VRPTW. Les deux articles considèrent des lois continues pour
les temps de trajet et de service.
Toutefois chacune des deux études a ses propres particularités. Miranda et Conceição [18] modé-
lisent le SVRPTW comme un CCP avec un taux de service α égal à 80%. Ainsi, les temps de service
et de trajets suivent des lois normales avec des espérance égales à leurs valeurs déterministes et la
variance est déduite à partir des coecients de variations générées par une loi uniforme U [0.1; 0.6].
Le tableau A.4 montre les métriques suivantes : nombre de véhicules, la distance moyenne parcourue,
les niveaux de services (NS) et les écarts entre le niveau de service (ENS) calculé par simulation et
celui prédit par l'approximation de AT i. On peut remarquer que MPMA utilise moins de véhicules
en moyenne qu'ILS avec une distance parcourue beaucoup plus réduite. En plus MPMA est le seul
capable de garantir le niveau de service requis pour toutes les instances (α = 80%).
D'une autre part, le travail de Nguyen et al. [19] utilise des temps de trajets avec une distribution
de probabilité Gamma. L'espérance des variables est égale à la valeur déterministe et l'écart type
est de 50% l'espérance. Le tableau A.5 résume les résultats pour le nombre moyen de véhicules,
le coût de la solution (distance), et le retard moyen (RA) chez chaque client. Dans un objectif de
comparaison, le MPMA est modié pour n'utiliser que le coût du recours sans tenir compte des
contraintes probabilistes. Le retard minimal moyen est atteint par MPMA de même que le coût
moyen minimale. Ceci est réalisé au détriment d'une petite augmentation sur le nombre de véhicules
en comparaison avec les diérentes versions de la Recherche Tabu (TS) de Nguyen et al. [19]. Ces
auteurs considèrent le retard moyen comme objectif principal. On peut conclure que MPMA donne
les meilleurs résultats par rapport à ces deux travaux.
Le modèle proposé dans le chapitre 4 ainsi que la méthode de solution MPMA ont montré leur
exibilité. Les comparaisons avec d'autres méthodes de la littérature donnent l'avantage à MPMA,
même si la méthode utilise des temps d'exécution légèrement supérieurs. Il est également important de
noter que l'approximation de AT i avec des loi Log-normales permet d'avoir des résultats compétitifs
pour les problèmes même si les temps de trajet (et de service) sont distribués par des loi normales où
147
A.3. UN ALGORITHME PARALLÈLE POUR LES VRP AVEC TEMPS DE TRAJET ETTEMPS DE SERVICE STOCHASTIQUES
Table A.5 : Comparaison de MPMA avec la TS de Nguyen et al. [19]
InstanceMéthode# Veh. MoyenDC MoyenRA MoyenTemps (s)C1 TS (AD1) 10.33 944.55 2.82 9.20
TS (AD2) 10.33 929.85 3.43 7.94TS (AD3) 10.22 918.61 4.19 8.62MPMA 9.89 908.73 5.55 51.41
C2 TS (AD1) 3.25 654.41 0.00 14.34TS (AD2) 3.13 648.60 0.04 9.97TS (AD3) 3.00 638.40 0.02 8.52MPMA 3.00 606.30 0.08 51.60
R1 TS (AD1) 15.67 1521.12 30.77 16.10TS (AD2) 15.83 1495.02 24.02 14.96TS (AD3) 15.58 1447.70 26.02 18.95MPMA 15.51 1409.47 19.25 51.76
R2 TS (AD1) 3.45 1134.96 5.33 51.50TS (AD2) 3.55 1050.33 1.48 42.38TS (AD3) 3.45 1086.00 1.77 43.90MPMA 3.99 991.07 1.96 53.56
RC1 TS (AD1) 15.25 1736.57 49.79 11.24TS (AD2) 15.38 1713.98 34.81 10.78TS (AD3) 15.13 1700.99 41.07 9.46MPMA 15.86 1630.91 22.55 51.71
RC2 TS (AD1) 4.00 1396.98 4.95 33.49TS (AD2) 3.88 1296.20 2.64 25.82TS (AD3) 4.00 1234.10 3.17 23.41MPMA 4.56 1182.94 3.86 52.67
Avg. TS (AD1) 8.91 1241.83 15.91 23.48TS (AD2) 8.95 1198.80 11.35 19.46TS (AD3) 8.82 1181.67 12.92 19.98MPMA 9.04 1131.34 9.19 52.16
Gamma2. Il reste dans la continuité de ce travail de tester d'autres types de recours, et l'extensions
du problème pour considérer les temps de trajets corrélés. Aussi, l'approximation de AT i peut être
exploitée dans des méthodes exactes de type Branch-and-Price. Finalement, on prévoit d'étudier des
problèmes multi objectif dont le niveau de service est un objectif et pas une contrainte.
Des résultats présentés dans ce chapitre ont été présentés à la conférence MIM2016 :
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2016)
A multi population memetic algorithm for the vehicle routing problem with time windows and sto-
chastic travel and service times
In 8th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2016 Troyes,
France, 2830 June 2016
Une extension de ce travail pour résoudre le problème avec une méthodes exacte a été présentée à
la conférence CLAIO2016 :
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2016)
An approximate column generation for the vehicle routing problem with hard time windows and sto-
chastic travel and service times.
In XVIII CLAIO, the Latin-Iberoamerican Conference on Operations Research Santiago de Chile,
Chile, 26 October 2016.
Un article regroupant les résultats de ce chapitre a été accepté pour publication dans le journal
Computers & Industrial Engineering.
2Avec des coecients de variation de plus de 35%
148
ANNEXE A. RÉSUMÉ EN FRANÇAIS
A.4 Gestion des ressources pour la maintenance d'un parc d'éo-
liennes
Le chapitre 5 introduit un problème d'aectation des ressources pour des activités de maintenance
pour un parc d'éoliennes. Le problème étudié sert pour montrer la présence d'objectifs conictuels
associés à diérentes parties prenantes aux parcs éoliens.
L'énergie éolienne est l'une des sources les plus importantes d'énergies renouvelables. Selon le
Conseil Mondial de l'Énergie Éolienne (GWEC en anglais) [6], l'année 2016 s'est achevée avec une
puissance installée de près de 487 GW, soit une croissance de 12,6% par rapport à 2015. Les projections
du GWEC montrent que la capacité d'énergie éolienne augmentera de près de 70% au cours des cinq
prochaines années. Ainsi, on prévoit que d'ici 2021, la capacité installée d'énergie éolienne atteindra
près de 830 GW. Malgré la croissance rapide des installations oshore [6], l'onshore représentait en
2015 près de 97,2% de la capacité éolienne mondiale totale [5]. Les activités d'exploitation et de
maintenance (O&M en anglais) pour les parcs éoliens sont très importantes. En fait, selon [9] l'O&M
représente entre 25% et 30% du coût total de la production d'énergie, et de 25% à 33% du coût total
du cycle de vie d'un parc éolienne. Il est donc indispensable d'optimiser ces coûts pour améliorer
la compétitivité des prix d'énergie et l'attractivité de ces projets. Les activités de maintenance sont
eectuées pour garantir le fonctionnement du système. Leur objectif est non seulement de corriger
mais aussi de prévenir les pannes du système an de retarder le plus possible leur apparition.
Toutefois, pendant ces activités les éoliennes doivent être arrêtés, ce qui conduit à une perte de
production d'énergie. Ainsi, l'exécution de ces activités peut aussi être couteuse si elles sont exécutées
très régulièrement. Par conséquent, il faut trouver un équilibre entre minimiser le risque de défaillance
et le coût total de maintenance (y compris la perte de production liée à l'indisponibilité). De plus
l'interaction de multiples acteurs impliqués dans les parcs éoliens peut impacter la façon dont les
activités de maintenance sont menées. Par exemple, l'agent en charge des O&M veut minimiser ses
coûts tandis que l'investisseur a intérêt à produire la plus grande quantité d'énergie [11]. De manière
générale on peut distinguer trois niveaux de décision, stratégique, tactique et opérationnel dans ce
domaine.
Le niveau opérationnel concerne des décisions quotidiennes. Par exemple, l'ordonnancement des
tâches de maintenance concerne ce niveau de décision. Dans ce dernier, la disponibilité des ressources
joue un rôle central. Kovàcs et al. [16] décrivent l'ordonnancement des tâches de maintenance comme
un composant majeur qui aecte la disponibilité et les coûts des turbines. Dans cet échelon il est
supposé que les actions de maintenance sont connues avant leur exécution car elles auraient été choisis
par une stratégie de maintenance. Aussi, puisque l'horizon de plan est limité à très peu de jours, il
est courant de supposer que les paramètres stochastiques tels que la vitesse du vent ou la hauteur
des vagues sont parfaitement prédits. Des travaux pour l'onshore et l'oshore ont été publiés dans la
littérature. Les objectifs sont normalement associés à la minimisation des coûts. Certaines références
traitent la maximisation de la production énergétique (ou la minimisation des pertes de production)
surtout dans le contexte onshore. La plupart des travaux utilisent des méthodes exactes (surtout
l'optimisation linéaire en nombres entiers), en revanche les paramètres tels que la durées des activités
ou les conditions météorologiques sont déterministes. Aussi, il n'existe pas de travaux qui considèrent
plusieurs objectifs au même temps. Pour combler cet écart de la littérature, le problème suivant est
presenté.
A.4.1 Ordonnancement multi objective pour le problème de maintenance
des éoliennes
Le problème d'ordonnancement des ressources dans un contexte onshore est traité dans ce chapitre.
Deux acteurs sont considérés dans ce travail : l'opérateur O&M et l'investisseur du parc. Chacun
149
A.4. GESTION DES RESSOURCES POUR LA MAINTENANCE D'UN PARC D'ÉOLIENNES
cherche à satisfaire son propre objectif, à savoir, minimiser les coûts d'une part et maximiser l'énergie
produite de l'autre part. Le problème considère un ensemble d'éoliennes pour lesquelles des tâches
de maintenance doivent être exécutées. Un nombre limité de techniciens ayant chacun leurs propres
compétences est dédié à la réalisation de ces tâches. Pendant l'exécution des tâches de maintenance,
les éoliennes sont arrêtées et ne produisent donc pas d'énergie. De plus, toute tâche commencée doit
être achevée. Ainsi, à la n de chaque journée, si la tâche n'est pas encore nie, les techniciens la
poursuivront le lendemain.
Chaque tâche est associée à une fenêtre de temps dont l'ouverture peut correspondre par exemple à
la date de disponibilité des matériaux nécessaires à l'éxécution de la tâche de maintenace. La fermeture
de la fênetre est considérée comme une date à laquelle la tâche devrait nir. Si la fenêtre de temps n'est
pas respectée, une pénalité est appliquée. Chaque tâche nécessite un certain nombre de compétences
et de techniciens par compétence. Le temps est discrétisé, et pendant chaque créneau de temps les
éoliennes qui ne sont pas en maintenance produisent de l'énergie. Pour chaque créneau, la vitesse du
vent est supposée connue ce qui permet de savoir si les conditions permettent ou non d'eectuer la
tâche prévue. Dans le cas où la vitesse est trop élevée, les techniciens doivent arrêter leur travail.
En plus le salaire des techniciens dépend de leurs compétences. Trois coûts sont considérés dans ce
chapitre, le coût lié à l'aectation de techniciens surqualiés, le coût lié aux heures supplémentaires,
et la pénalité en cas de retard d'exécution de la tâche de maintenance.
Algorithm 9 Epsilon Constraints adapté pour la maximisation de Z = (Z1,−Z2)
Require: numSteps1: Solutions← 2: z1∗, z2. ← maxZ1
3: z1∗, z2∗ ← max−Z2| (Z1 = z1∗)
4: Solutions← Solutions ∪ z1∗, z2∗5: z1., z2∗∗ ← max−Z2
6: stepSize← (z2∗−z2∗∗)
numSteps
7: i← 18: while (z2
∗ − i · stepSize) > z2∗∗ do
9: z1∗, z2. ← maxZ1| (Z2 ≤ z2∗ − i · stepSize)10: z1∗, z2∗∗∗ ← max−Z2| (Z1 = z1
∗)11: Solutions← Solutions ∪ z1∗, z2∗∗∗12: i← i+ 113: end while
14: Sort and check(Solutions)
Un modèle bi-objective est présenté pour résoudre le problème. Les deux objectifs sont dénis par
Z1 et Z2 et cherchent à maximiser la quantité d'énergie produite tout en minimisant l'ensemble des
coûts respectivement. Il existe diérentes façons de résoudre les problèmes avec plusierus objectifs.
Dans cette étude une adaptation de la méthode epsilon constraints expliquée dans l'algorithme 9
est utilisée. La méthode itère pendant un nombre prédéni de pas. À chaque itération (lignes 8 à
13) l'algorithme cherche à optimiser chaque objectif (séparément) en utilisant des contraintes pour les
valeurs de l'autre objectif. Ceci permet de trouver des solutions Pareto optimales ou des solutions pour
lesquelles aucun objectif ne peut être amélioré sans endommager un autre. Les lignes 2 à 6 permettent
de limiter la recherche entre les bornes dénies par la quantité maximale d'énergie produite et le coût
minimal.
L'approche Epsilon Contraintes est testée sur les instances proposés par Froger et al. [11]. Certaines
modications ont été introduites pour tenir compte des heures supplémentaires. La gure A.5 présente
un exemple de solutions trouvées pour trois périodes (créneaux) par jour et une seule compétence.
De plus, l'instance contient 20 tâches et considère une période de cinq jours. Un total de 16 solutions
non dominées sont trouvés mais seulement 2 sont prouvées optimales. Cela signie que l'exécution
des problèmes linéaires pour optimiser z1 et z2 s'est arrêtée à cause de la limite imposée sur le temps
d'exécution avant de garantir l'optimalité. Toutefois, les écarts moyen sont très faibles et c'est possible
150
ANNEXE A. RÉSUMÉ EN FRANÇAIS
89000
89500
90000
90500
91000
91500
92000
92500
93000
100 150 200 250 300 350
Energy
Costs
Figure A.5 : Solutions Pareto optimales pour l'instance 10_2_1_20_B_5 de Froger et al. [11]
que toutes les 16 solutions soient ecaces. La plupart des instances présentent des résultats similaires.
Il convient de noter que pour l'exemple, une augmentation de 115% des coûts peut avoir un impact de
près de 4% sur la production d'énergie. Ces résultats montrent le conit entre les objectifs des diérents
acteurs du projet. Ils permettent également de mettre en évidence l'importance de la planication et
l'ordonnancement des tâches de maintenance pour produire la quantité maximale d'énergie. Sur la
base de ces résultats il est proposé d'étendre ce problème sur un horizon de planication long qui
inclus les stratégies de maintenance.
Les résultats préliminaires de ce chapitre ont été présentés à la conférence IEOM :
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2017)
Wind farm maintenance scheduling model and solution approach
In Proceedings of the International Conference on Industrial Engineering and Operations Management
Bogota, Colombia, 2526 October, 2017.
A.5 Sélection des stratégies de maintenance et relation avec
l'ordonnancement
Le problème d'ordonnancement introduit dans le chapitre 5 est étendu pour étudier les interactions
entre les stratégies de maintenance et les problèmes opérationnels. En eet, la sélection de la politique
de maintenance peut avoir des eets considérables sur le coût d'O&M et la production énergétique.
En même temps la composante opérationnelle (ordonnancement des tâches) peut altérer le plan de
maintenance. Une procédure de simulation est conçue pour évaluer ces interactions. Toutefois, car le
problème du chapitre 5 est trop complexe, il est simplié et résolu par de simples règles heuristiques
pour aecter les ressources aux tâches.
Le niveau stratégique concerne des décisions prises pour plusieurs années ou même pour le cycle
de vie du projet. Ce niveau inclus l'emplacement du parc, la disposition des éoliennes, ou le type de
politique de maintenance. Une politique de maintenance détermine à quelles dates intervenir et quel
type de maintenance doit être fait sur les éoliennes. Deux types principaux de stratégies existent selon
Shaee [23] : la maintenance préventive et corrective. La dernière consiste à intervenir lorsqu'une
défaillance est constatée. Les activités de maintenance préventives sont réalisées avant la survenue
d'une panne dans le but de retarder son apparition. La maintenance préventive peut être divisée en
maintenance systématique, exécutée périodiquement et maintenance conditionnelle qui est réalisée en
151
A.5. SÉLECTION DES STRATÉGIES DE MAINTENANCE ET RELATION AVECL'ORDONNANCEMENT
fonction de l'état du système. En plus, des actions opportunistes peuvent être aussi considérées. Il
s'agit des activités préventives qui cherchent à proter des arrêts du système à cause d'une panne ou
à d'une autre activité préventive. Cela permet de diminuer les coûts de transport, ou de la mise en
place d'équipement pour eectuer les entretiens. Les activités opportunistes peuvent se faire dans la
même éolienne ou sur d'autres à proximié. Pour sélectionner quel type de stratégie est meilleure, la
plupart des études utilisent de la simulation pour estimer les coûts et la production énergétique.
Le problème est déni avec un ensemble de turbines qui nécessitent des tâches de maintenance. De
plus, chaque éolienne est composée de plusieurs sous-systèmes. Chaque sous-système est soumis à un
processus de détérioration conduisant à une défaillance. Les sous-systèmes sont considérés indépen-
dants entre eux. Deux types de maintenance sont considérés : corrective et systématique. Quand des
tâches correctives sont mise en place, les composants en panne sont remis à neuf. En revanche, quand
les activités sont de type préventif, la réparation est imparfaite.
Un ensemble limité de techniciens ayant tous les mêmes compétences est considère. Les techniciens
se déplacent vers les éoliennes pour eectuer les entretiens. Les considérations de sécurité et exécution
des tâches de maintenance présentés pour le problème d'ordonnancement sont aussi prises en compte
dans cette partie. Il faut rajouter que le temps d'exécution de chaque tâche est une variable aléatoire.
Un horizon de 15 ans est utilisé et deux stratégies de maintenance sont examinées. La première est
une stratégie purement corrective qui planie des actions de maintenance lorsqu'une panne se produit
dans une éolienne. La deuxième stratégie combine des action correctives et des actions préventives
périodiques. Quelque soit le type de stratégie, il est supposé que deux fois par an des activités d'en-
tretien son faites pour chaque éolienne. Ces activités considèrent les changements de consommables
comme l'huile, etc.
Une méthode de simulation à événements discrets est utilisée pour simuler et comparer les deux
stratégies. En plus, deux règles de décisions sont utilisées pour aecter les techniciens aux tâches de
maintenance. La première aecte les ressources dès qu'elles sont disponibles pour eectuer les tâches
sans aucune autre considération (O1). La deuxième utilise les prévisions de vitesses de vent pour
sélectionner le moment où il est préférable d'exécuter les tâches pour réduire les pertes de production
d'énergie. En plus les tâches doivent toujours démarrer en début de période.
Des modules pour simuler la vitesse de temps, les techniciens, et les composants (et leurs pannes)
des éoliennes ont été programmés. Ainsi un module est responsable de la gestion des ressources selon
les règles d'aectation. En combinant les deux stratégies de maintenance avec les deux règles de
décisions quatre scénarios sont considères : "`CM-O1"', "`CM-O2"', "`PM-O1"', et "`PM-O2"'. Chaque
combinaison est simulée pour un parc éolien constitué de 100 éoliennes. Des simulations du parc pour
un horizon de quinze ans sont considérées, de plus, une centaine de répliques sont faites pour collecter
les résultats.
La comparaison des stratégies se fait par rapport aux coûts de maintenance, énergie produite,
le nombre de pannes et le pourcentage du temps que les turbines sont disponibles pour produire
de l'énergie. Il est supposé que les coûts associés aux tâches correctives sont plus élevés que ceux
correspondant aux préventives. Les résultats montrent que les stratégies préventives conduisent à une
réduction des coûts par rapport aux stratégies correctives. En eet, en comparant chaque stratégie,
les coûts sont plus élevés pour la règle O2, donc, celle-ci conduit à une augmentation des pannes des
composantes. Cette situation peut être expliquée par le fait que les techniciens sont souvent aectés
d'une façon plus étendue sur le temps avec O2 et non pas dès qu'une tâche de maintenance arrive.
La règle O2 génére une production d'énergie plus grande que la règle O1 ce qui n'est pas sur-
prenant en regardant le principe de chacune. En eet, ces résultats peuvent être expliqué par le
fait que O2 empêche de planier des tâches tardives. Par exemple, la réglé O2 n'aectera jamais les
techniciens pour démarrer une tâche le vendredi après-midi, ce qui conduira à une perte d'énergie
produite pendant le week-end. En revanche, O1 pourrait parfaitement exécuter cette planication.
Ces résultats montrent un point très important : la quantité d'énergie produite et les coûts subissent
152
ANNEXE A. RÉSUMÉ EN FRANÇAIS
Figure A.6 : Coûts par stratégie et par règle d'assignation.
Figure A.7 : Énergie produite par stratégie et par règle d'assignation.
153
A.6. CONCLUSIONS
des changements considérables en fonction de la règle choisie.
De plus les décisions opérationnelles ainsi que les stratégiques ont un impact important sur ces
métriques. Donc les régles de décision utilisées pour faire l'aectation des ressources aux tâches de
maintenance semblent être une piste importante à explorer. D'autres règles de décision sont requises
d'être testées pour améliorer les tâches de maintenance.
A.6 Conclusions
Cette thèse est dédiée aux problèmes de tournées de véhicules avec incertitudes et les problèmes
de planication de la maintenance pour les éoliennes. La plupart des travaux traitant ces problèmes
considèrent le cas déterministe en supposant que les informations sont connues à l'avance. Récemment,
une plus grande attention a été donnée aux versions stochastiques du fait de leur importance aussi
bien sur le plan académique qu'applicatif. En eet, il a été démontré que les conséquences d'ignorer
la nature stochastique des problèmes sont importantes et peuvent impacter les coûts, l'indisponibilité
des systèmes, ou l'insatisfaction des clients. Dans ce contexte, les contributions de cette thèse sont
les suivantes : D'abord, des méthodes de résolutions ecaces, basées sur des algorithmes mémétiques,
sont présentées pour deux VRP avec incertitudes. Pour gérer la nature stochastiques du problème,
des procédures adéquates ont été développées pour ces derniers. Il s'agit du VRP avec demandes
stochastiques, et du VRP avec temps de trajet et de services stochastiques et de fenêtres de temps
dures. De plus, de nouveaux meilleurs résultats sont présentés pour les deux problèmes. Le problèmes
de planication de tâches de maintenance pour un parc d'éoliennes est également étudié dans cette
thèse dans un contexte multicritère. Après avoir proposé une formulation mathématique du problème,
une méthode de type epsilon contraintes a été conçue pour le résoudre. Une variante intégrant la
sélection des stratégies de maintenance à l'aectation des ressources (techniciens) aux tâches est
également analysée en utilisant une méthode basée sur la simulation à évènements discrets.
Pour le VRP avec demandes stochastiques, un algorithme méméthique combiné à une procédure de
redémarrage basée sur une méthode de type GRASP a été proposée. Des résultats sont donnés aussi
bien sur les instances couremment utilisés dans la littérature que sur un nouvel ensemble d'instances
comportant un plus grand nombre de clients. Ces derniers résultats pourront servir de points de com-
paraison pour des travaux futurs, il pourront également être utilisés pour évaluer d'autres approches
telles que la réoptimisation ou des méthodes faisant appel à de nouvelles formes de recours.
Le VRP avec temps de trajet et de service stochastiques et fenêtres de temps dures a été également
examiné dans cette thèse. Plusieurs populations ont été utilisées dans un algoritme mémétique parallèle
conçu pour résoudre ce problème. Ces populations considèrent chacune des choix d'approximation des
paramètres spéciques, et partagent des informations en échangeant des individus à des temps précis
dans l'algorithme. Des comparaisons à d'autres méthodes publiées sur le sujet démontrent que notre
approche est ecace et éxible pour résoudre le problème considéré ainsi que d'autres variantes pour
lesquelles des résultats sont disponibles dans la littérature.
Le problème de planication de la maintenance pour les éoliennes a été d'abord étudié dans un
contexte déterministe. En utilisant un modèle linéaire bi-critère à variables mixtes résolu grace à
la méthode epsilon contraintes, il a été démontré que les coûts de maintenance et la production
énergétique peuvent être en conit même dans le cas opérationnel. Une intégration de ce problème
au niveau de décision stratégique permet de conclure que la façon de gérer la planication de la
maintenance et la sélection des stratégies de maintenance ont un impact important sur les coûts et la
production énergétique. Dans ce dernier travail, l'apparition des pannes et le temps nécessaire pour
la réparation sont considérés comme stochastiques.
Les travaux futurs autour de VRP stochastiques et la planication de la maintenance des éoliennes
doivent se concentrer sur l'interdépendance des variables (demandes, dégradation, temps de trajet). De
plus, plus d'approches statistiques pour valider les lois de probabilités utilisées pour les paramètres des
154
ANNEXE A. RÉSUMÉ EN FRANÇAIS
problèmes seront bienvenues. Des études de cas seront nécessaires pour montrer la valeur des solutions
stochastiques dans des applications réelles. Pour ces dernières, nous nous attendons à une croissance
du nombre de méthodes approchées pour les résoudre. La conception d'instances standardisées pour
les VRP stochastiques est également essentielle pour permettre une comparaison rigoureuse entre
diérents modèles et méthodes de résolution.
Enn, nous pensons que l'optimisation robuste connaitra un développement important pour com-
plémenter et enrichir ce domaine de recherche quand les lois de probabilités sont inconnues. Nous
espérons que les pistes de recherche proposées et que les travaux présentés dans cette thèse serviront
pour de noubreux développements futurs.
155
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158
Appendix B
List of contributions
International peer-reviewed journal papers
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2018). A multi-population algorithm to solve the
VRP with stochastic service and travel times. Computers & Industrial Engineering, 125, 144-156.
https://doi.org/10.1016/j.cie.2018.07.042. http://www.sciencedirect.com/science/article/
pii/S0360835218303668
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. (2018). A Hybrid metaheuristic algorithm
for the vehicle routing problem with stochastic demands. Computers & Operations Research,
99, 135-147. https://doi.org/10.1016/j.cor.2018.06.012. http://www.sciencedirect.com/science/
article/pii/S0305054818301667
Peer-reviewed conference proceedings
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. Wind farm maintenance scheduling model and
solution approach, Proceedings of the IEOM Conference Bogota, Colombia, 2526 October 2017
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. A multi population memetic algorithm for
the vehicle routing problem with time windows and stochastic travel and service times, 8th IFAC
Conference on Manufacturing Modelling, Management and Control MIM 2016 Troyes, France,
2830 June 2016
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. A Memetic Algorithm for the Vehicle Routing
Problem with Stochastic Demands, Proceedings of the 45th International Conference on Computers
& Industrial Engineering (CIE45) Metz, France, 2830 October 2015
Conference presentations
Gutierrez, A., Dieulle, L., Labadie, N., Velasco, N. An approximate column generation for the vehicle
routing problem with hard time windows and stochastic travel and service times. XVIII CLAIO, the
Latin-Iberoamerican Conference on Operations Research Santiago de Chile, Chile. 26 October
2016.
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