issues in dynamic fleet management talk at route 2000 - international workshop on vehicle routing...
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Issues in Dynamic Fleet Issues in Dynamic Fleet ManagementManagement
Talk atTalk at
ROUTE 2000 - INTERNATIONAL WORKSHOP ROUTE 2000 - INTERNATIONAL WORKSHOP ON VEHICLE ROUTINGON VEHICLE ROUTING
SKODSBORG, DENMARK - AUGUST 16-19, 2000SKODSBORG, DENMARK - AUGUST 16-19, 2000
Geir HasleGeir Hasle
Research Director, Department of Research Director, Department of OptimizationOptimization
SINTEF Applied MathematicsSINTEF Applied Mathematics
Oslo, NorwayOslo, [email protected]@math.sintef.no
http://www.oslo.sintef.no/am/http://www.oslo.sintef.no/am/
My talkMy talk
SINTEF Applied Mathematics SINTEF Applied Mathematics (SAM)(SAM)
Fleet ManagementFleet Management– industrial potential, status, requirementsindustrial potential, status, requirements– technologytechnology– research, scienceresearch, science– bridging the gap between science and industrybridging the gap between science and industry
ChallengesChallenges Routing etc. at SAMRouting etc. at SAM Research AgendaResearch Agenda
3
SINTEFThe Foundation for Scientific and Industrial Research at the Norwegian Institute of Technology
Business concept:SINTEF´s goal, in co-operation with NTNU and UiO, is to meet needs of the private and public sectors for research-based innovation and development
The vision:Technology for a better society
Locations:The SINTEF Group have 1800 employees, 400 in Oslo and 1400 in Trondheim.
SINTEF Electronics and CyberneticsSINTEF Electronics and Cybernetics
SINTEF Applied MathematicsSINTEF Applied Mathematics
SINTEF Civil and Environmental EngineeringSINTEF Civil and Environmental Engineering
SINTEF Applied ChemistrySINTEF Applied Chemistry
SINTEF Materials TechnologySINTEF Materials Technology
SINTEF Industrial ManagementSINTEF Industrial Management
SINTEF Telecom and InformaticsSINTEF Telecom and Informatics
SINTEF UnimedSINTEF Unimed
SINTEF Petroleum ResearchSINTEF Petroleum Research
MARINTEKThe Norwegian Marine Technology Research Institute
MARINTEKThe Norwegian Marine Technology Research Institute
SINTEF Energy ResearchSINTEF Energy Research
SINTEF Fisheries and AquacultureSINTEF Fisheries and Aquaculture
President/Vice-president
President/Vice-president
SINTEFs board
SINTEFs board
SINTEFscouncil
SINTEFscouncil
SINTEF-Group turnover in SINTEF-Group turnover in 19991999
Strategic programs from The Research Council of Norway 4,3%
Contracts 92,4% - Industrial and commercial
enterprises 53,0% - Public sector 12,5% - International contracts
10,5% - The Research Council of
Norway (project grants)
9,9% - Other sources 6,5%
Basic grants from The Research Council of Norway 3,3%
SINTEF Applied MathematicsSINTEF Applied Mathematicshttp://www.oslo.sintef.no/amhttp://www.oslo.sintef.no/am
A contract research institute in the A contract research institute in the SINTEF groupSINTEF group
Geometry
Modeling
Optimisation
Simulation
Focus:Applied research
PlanningDecision Support
Focus:Applied research
PlanningDecision Support
Basis:Knowledge Based Systems
Operations ResearchComputing science
Basis:Knowledge Based Systems
Operations ResearchComputing science
Application types:Resource optimisationDesign/configuration
Discrete
Application types:Resource optimisationDesign/configuration
Discrete
Main business areas:Transportation
Area managementOil business
Manufacturing
Main business areas:Transportation
Area managementOil business
Manufacturing
SINTEF Applied Mathematics Department of OptimisationSINTEF Applied Mathematics
Department of Optimisation
Approach:Generic Tools
ReuseMethodology
Approach:Generic Tools
ReuseMethodology
Transportation of goods in Transportation of goods in NorwayNorwayand EUand EU
12.000 companies (EU 1/2 mill.)12.000 companies (EU 1/2 mill.) Annual turnover 30 billion. (EU 1.200 billion.)Annual turnover 30 billion. (EU 1.200 billion.) Many SMEsMany SMEs 36 % empty driving36 % empty driving Capacity utilization with load: 60 %Capacity utilization with load: 60 % Logistics costs 12% of product costs (EU 7%)Logistics costs 12% of product costs (EU 7%) EU: 13 million trucks, 800 billion ton-kilometers EU: 13 million trucks, 800 billion ton-kilometers
(1990)(1990) Germany: freight income some 60 billion DM (1990)Germany: freight income some 60 billion DM (1990)
Industrial use of VRP Industrial use of VRP ToolsTools
Excess travel, huge potentialExcess travel, huge potential Swedish report* 1999 (commercial road Swedish report* 1999 (commercial road
transport)transport)– large end users, food & beveragelarge end users, food & beverage– generation of static routesgeneration of static routes– vendors claim operational toolsvendors claim operational tools– very high potential for savingsvery high potential for savings
* A. Henriksson, P. Liljevik: ”Dynamisk ruttplanlegging i verkligheten”* A. Henriksson, P. Liljevik: ”Dynamisk ruttplanlegging i verkligheten”
Minirapport MR 123, TFK - Institutet för transportforskning, Stockholm October 1999Minirapport MR 123, TFK - Institutet för transportforskning, Stockholm October 1999
Increasing need for VRP Increasing need for VRP ToolsTools
focus on focus on – timetime– costcost– utilizationutilization– customer servicecustomer service– lead time reductionlead time reduction– reactivityreactivity
regulations, environmental concernsregulations, environmental concerns e-commerce, home shoppinge-commerce, home shopping
Reasons for mismatchReasons for mismatch lack of awareness in industrylack of awareness in industry lack of data and infrastructurelack of data and infrastructure price (SMEs)price (SMEs) organizational problems, resistanceorganizational problems, resistance practical constraintspractical constraints
– information availabilityinformation availability– physical movementphysical movement
tools not good enoughtools not good enough– functionality, modelling powerfunctionality, modelling power– user friendlinessuser friendliness– integrationintegration– logistical performancelogistical performance
Existing tools - keywords Existing tools - keywords • Large variety: simple TSP - sophisticated VRP solversLarge variety: simple TSP - sophisticated VRP solvers• focus: road transportation of goods, local distributionfocus: road transportation of goods, local distribution• built for operative planning, used for generation of static built for operative planning, used for generation of static
routesroutes• packagespackages• primitive integration, but good import facilitiesprimitive integration, but good import facilities• inflexible and simple or heavy on consultancyinflexible and simple or heavy on consultancy• Windows-platformWindows-platform• good user interfaces, map visualization, manual changesgood user interfaces, map visualization, manual changes
• VRP algorithms?VRP algorithms?• real-time planning?real-time planning?• multiple users?multiple users?• continuous optimization?continuous optimization?
• priced at USD 40.000 and above (high end)priced at USD 40.000 and above (high end)
Some VendorsSome Vendors • Descartes Systems Descartes Systems USAUSA• Caps Logistics -> BaanCaps Logistics -> Baan USAUSA• MicroAnalytics MicroAnalytics USA/GBUSA/GB• Roadnet Technologies (UPS) Roadnet Technologies (UPS) USAUSA• i2 i2 USAUSA• ESRI ESRI USAUSA• Kositzky and Associates Kositzky and Associates USAUSA• Manugistics Manugistics USAUSA• Carrier Logistics Inc Carrier Logistics Inc USAUSA• Insight Inc. Insight Inc. USA/The USA/The
Netherlands/GBNetherlands/GB• Caliper Corporation Caliper Corporation USAUSA• Trapeze Software Group Trapeze Software Group USA/CanadaUSA/Canada
• Giro Giro CanadaCanada• DPS International DPS International UKUK• Paragon Software Systems Paragon Software Systems UKUK• Optrak (Andersen Consulting) Optrak (Andersen Consulting) UKUK• Ilog Ilog FF• Diagma Diagma FF• PTV PTV DD• AlfaplanAlfaplan DD• PLSPLS DD• PrologosPrologos DD
Typically claim 10 - 30% cost reductions - static routesTypically claim 10 - 30% cost reductions - static routes
Few VRP Tools in Few VRP Tools in Operation in NorwayOperation in Norway
Coca-ColaCoca-Cola Taxi companiesTaxi companies FalkenFalken NASNAS NKLNKL Tollpost-GlobeTollpost-Globe LinjegodsLinjegods Postal servicePostal service Hydro AgriHydro Agri
Challenges - VRP Challenges - VRP ToolsTools
FunctionalityFunctionality ModellingModelling
– constraintsconstraints– criteriacriteria– uncertaintyuncertainty– dynamicsdynamics– supply-chain coordinationsupply-chain coordination
AdaptabilityAdaptability Power: speed vs. qualityPower: speed vs. quality Large-size problemsLarge-size problems User InterfaceUser Interface IntegrationIntegration Support etc.Support etc.
Dynamic, real-time routing Dynamic, real-time routing - Success stories?- Success stories?
Paragon - TescoParagon - Tesco– “… “… Home shoppers simply log onto the Home shoppers simply log onto the
dedicated area of Tesco's website, select their dedicated area of Tesco's website, select their purchases and identify a two hour time window purchases and identify a two hour time window for delivery to an address of their choosing” ...for delivery to an address of their choosing” ...
TruckstopsTruckstops– “… “… In some UK applications it is even used to In some UK applications it is even used to
recalculate routes during the day, modifying recalculate routes during the day, modifying its original calculations to take account of new its original calculations to take account of new requirements and reflecting data transmitted requirements and reflecting data transmitted back from vehicles by radio” …back from vehicles by radio” …
PriceWaterhouseCoopersPriceWaterhouseCoopers
Goal - VRP TechnologyGoal - VRP Technology
real benefits for industry - real benefits for industry - logistical performancelogistical performance– solve right problemsolve right problem– plan quality vs. response timeplan quality vs. response time– user interaction, user-friendlinessuser interaction, user-friendliness– configurabilityconfigurability– reactivityreactivity– priceprice
Future VRP technologyFuture VRP technology GIS vendorsGIS vendors ERP vendorsERP vendors ASP solutions, thin clients, Internet, wwwASP solutions, thin clients, Internet, www
better tools for strategic/tactical planningbetter tools for strategic/tactical planning supply-chain coordination, integrated supply-chain coordination, integrated
solutionssolutions
dynamic, real time fleet managementdynamic, real time fleet management
Dynamic Fleet Dynamic Fleet Management - Management - PrerequisitesPrerequisites
ICT infrastructureICT infrastructure– order dataorder data– fleet datafleet data
access to high quality traffic dataaccess to high quality traffic data– speed predictionsspeed predictions– ““organic” electronic road networkorganic” electronic road network
Better understanding of routing policiesBetter understanding of routing policies Better VRP algorithmsBetter VRP algorithms
Issues in VRP researchIssues in VRP research Large, ill-structured problemsLarge, ill-structured problems rich modelsrich models
– uncertaintyuncertainty– dynamicsdynamics– multiple criteriamultiple criteria
reactivityreactivity– disruption?disruption?– slackslack– policiespolicies
plan quality vs. response time plan quality vs. response time performanceperformance
decompositiondecomposition human issueshuman issues
Stochastic and dynamic VRPsStochastic and dynamic VRPs
what does “dynamic” mean?what does “dynamic” mean?– problem changes dynamicallyproblem changes dynamically– Psaraftis (1995): “... information on the problem is made known to Psaraftis (1995): “... information on the problem is made known to
the decision maker or is updated concurrently with the the decision maker or is updated concurrently with the determination of the set of routes.”determination of the set of routes.”
– Baita, Ukovich, Pesenti, Favaretto (1998): “... releated decisions Baita, Ukovich, Pesenti, Favaretto (1998): “... releated decisions have to be taken at different times within some time horizon, and have to be taken at different times within some time horizon, and earlier decisions influence later decisions.”earlier decisions influence later decisions.”
– a.k.a. “real-time”, “on-line”a.k.a. “real-time”, “on-line”
““organic” routing plansorganic” routing plans– challengeschallenges
information flowinformation flow physical goodsphysical goods
– good idea? (talk of Carlos Daganzo)good idea? (talk of Carlos Daganzo)
Literature - dynamic Literature - dynamic VRPsVRPs
6 INFORMS sessions since 1995, some 20 6 INFORMS sessions since 1995, some 20 paperspapers
some 50 journal paperssome 50 journal papers
Some papersSome papersPsaraftis (1995): Dynamic vehicle routing: Status and prospectsPsaraftis (1995): Dynamic vehicle routing: Status and prospects
Bertsimas, DJ / SimchiLevi, D (1996): A new generation of vehicle routing research: Robust algorithms, addressing uncertaintyBertsimas, DJ / SimchiLevi, D (1996): A new generation of vehicle routing research: Robust algorithms, addressing uncertainty
Crainic, TG / Laporte, G (1997): Planning models for freight transportationCrainic, TG / Laporte, G (1997): Planning models for freight transportation
Baita, F / Ukovich, W / Pesenti, R / Favaretto, D (1998): Dynamic routing-and-inventory problems: A reviewBaita, F / Ukovich, W / Pesenti, R / Favaretto, D (1998): Dynamic routing-and-inventory problems: A review
Swihart, MR / Papastavrou, JD (1999): A stochastic and dynamic model for the single-vehicle pick-up and delivery problemSwihart, MR / Papastavrou, JD (1999): A stochastic and dynamic model for the single-vehicle pick-up and delivery problem
Savelsbergh, M / Sol, M (1998): Drive: Dynamic routing of independent vehiclesSavelsbergh, M / Sol, M (1998): Drive: Dynamic routing of independent vehicles
Ioachim, I / Desrosiers, J / Soumis, F / Belanger, N (1999): Fleet assignment and routing with schedule synchronization constraintsIoachim, I / Desrosiers, J / Soumis, F / Belanger, N (1999): Fleet assignment and routing with schedule synchronization constraints
Gans, N / VanRyzin, G (1999): Dynamic vehicle dispatching: Optimal heavy traffic performance and practical insightsGans, N / VanRyzin, G (1999): Dynamic vehicle dispatching: Optimal heavy traffic performance and practical insights
Reiman, MI (1999): Heavy traffic analysis of the dynamic stochastic inventory-routing problemReiman, MI (1999): Heavy traffic analysis of the dynamic stochastic inventory-routing problem
Gendreau, M / Guertin, F / Potvin, JY / Taillard, E (1999): Parallel tabu search for real-time vehicle routing and dispatchingGendreau, M / Guertin, F / Potvin, JY / Taillard, E (1999): Parallel tabu search for real-time vehicle routing and dispatching
Powell, WB / Towns, MT / Marar, A (2000): On the value of optimal myopic solutions for dynamic routing and scheduling Powell, WB / Towns, MT / Marar, A (2000): On the value of optimal myopic solutions for dynamic routing and scheduling problems in the presence of user noncomplianceproblems in the presence of user noncompliance
Cheung, RK / Muralidharan, B (2000): Dynamic routing for priority shipments in LTL service networksCheung, RK / Muralidharan, B (2000): Dynamic routing for priority shipments in LTL service networks
Gendreau, M / Laporte, G / Seguin, R (1996): Stochastic vehicle routingGendreau, M / Laporte, G / Seguin, R (1996): Stochastic vehicle routing
Gendreau, M / Laporte, G / Seguin, R (1996): A tabu search heuristic for the vehicle routing problem with stochastic demands and Gendreau, M / Laporte, G / Seguin, R (1996): A tabu search heuristic for the vehicle routing problem with stochastic demands and customerscustomers
Haughton, MA (1998): The performance of route modification and demand stabilization strategies in stochastic vehicle routingHaughton, MA (1998): The performance of route modification and demand stabilization strategies in stochastic vehicle routing
Yang, WH / Mathur, K / Ballou, RH (2000): Stochastic vehicle routing problem with restockingYang, WH / Mathur, K / Ballou, RH (2000): Stochastic vehicle routing problem with restocking
Haughton, MA (2000): Quantifying the benefits of route reoptimisation under stochastic customer demandsHaughton, MA (2000): Quantifying the benefits of route reoptimisation under stochastic customer demands
Secomandi, N (2000): Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demandsSecomandi, N (2000): Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands
Shieh, HM / May, MD (1998): On-line vehicle routing with time windows - Optimization-based heuristics approach for freight Shieh, HM / May, MD (1998): On-line vehicle routing with time windows - Optimization-based heuristics approach for freight demands requested in real-timedemands requested in real-time
Kilby / Prosser / Shaw: Dynamic VRPs: A Study of Scenarios (forthcoming)Kilby / Prosser / Shaw: Dynamic VRPs: A Study of Scenarios (forthcoming)
Approaches - uncertainty, Approaches - uncertainty, dynamics dynamics
ignoreignore deterministic model - and repairdeterministic model - and repair
– crisp, optimized plans are brittlecrisp, optimized plans are brittle– is disruption costly?is disruption costly?– add slack, how?add slack, how?
stochastic modelstochastic model– investigation of policiesinvestigation of policies– still need dynamic decision-makingstill need dynamic decision-making
lessons to be learnt from factory schedulinglessons to be learnt from factory scheduling
Dynamic VRP DSSDynamic VRP DSS dependent on high quality updated dependent on high quality updated
informationinformation– fleet statusfleet status– order statusorder status
““organic” route planningorganic” route planning– concept of current planconcept of current plan– when do we commit?when do we commit?– when do we include changes?when do we include changes?– locking parts of planlocking parts of plan– do we need to worry about disruption?do we need to worry about disruption?– dependence on type of operation / business rulesdependence on type of operation / business rules
delivery vs. pickupdelivery vs. pickup
– applicable algorithmsapplicable algorithms– (how much) do we save by taking a dynamic approach?(how much) do we save by taking a dynamic approach?
ApproachesApproaches insertion heuristics + iterative insertion heuristics + iterative
improvementimprovement constraint propagation constraint propagation
MP formulations?MP formulations?
Minimal disruption possibly an additional Minimal disruption possibly an additional goal criterion componentgoal criterion component
Routing at SAMRouting at SAM
SPIDERSPIDER GreenTripGreenTrip HAMMER - vessel routing with inventory HAMMER - vessel routing with inventory
constraintsconstraints Bus schedulingBus scheduling eCSPlain, EU FP VeCSPlain, EU FP V Distributed problem solvingDistributed problem solving ProposalsProposals
SPIDERSPIDER
a VRP Solver C++ program librarya VRP Solver C++ program library– UNIXUNIX– WindowsWindows– COM componentCOM component
instantiates to a module for optimised instantiates to a module for optimised transport managementtransport management– plan-administrasjonplan-administrasjon– VRP optimisationVRP optimisation– cheapest path calculationscheapest path calculations
adaptable to wide variety of applicationsadaptable to wide variety of applications distribution through sw vendorsdistribution through sw vendors
GreenTripGreenTrip Esprit 20603, January 1996-March 1999, > 40 person-Esprit 20603, January 1996-March 1999, > 40 person-
yearsyears ConsortiumConsortium
– Tollpost-Globe (N) Tollpost-Globe (N) – Pirelli (I)Pirelli (I)– Ilog (F)Ilog (F)– University of Strathclyde (GB) University of Strathclyde (GB) – SINTEF (N)SINTEF (N)
RTD effort in methods, algorithms, and generic sw for RTD effort in methods, algorithms, and generic sw for optimised fleet managementoptimised fleet management
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The goal of GreenTripThe goal of GreenTrip
Produce a Produce a cost-effectivecost-effective tool to tool to optimise routing of vehicles thatoptimise routing of vehicles that– is genericis generic– takes into account multiple business takes into account multiple business
constraintsconstraints– permits efficient (re)configuration permits efficient (re)configuration – integrates easily in existing IT integrates easily in existing IT
infrastructure infrastructure
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GreenTrip Technical GreenTrip Technical ApproachApproach
OO ProgrammingOO Programming Constraint ProgrammingConstraint Programming Iterative Improvement TechniquesIterative Improvement Techniques Applications ModellingApplications Modelling Automated Systems Automated Systems
(Re)Configuration (Re)Configuration
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The GreenTrip ConsortiumThe GreenTrip Consortium
SINTEFSINTEF
Tollpost-Globe
Tollpost-Globe PirelliPirelli
ILOGILOG UoSUoS
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CASE : TOLLPOST-GLOBECASE : TOLLPOST-GLOBE
Pick up orders : 600Pick up orders : 600 Regular and non-regular customersRegular and non-regular customers Deliveries : 2.400Deliveries : 2.400 Time windows - Customer serviceTime windows - Customer service Two days are not the sameTwo days are not the same some 100 vehiclessome 100 vehicles Different vehicles (size, volume, equipment)Different vehicles (size, volume, equipment) Depot with automatic sorting / registrationDepot with automatic sorting / registration
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CASE : TOLLPOST-GLOBECASE : TOLLPOST-GLOBE
Electronic road and address data are Electronic road and address data are available via the GIS Transportation available via the GIS Transportation DemonstratorDemonstrator
Mobile communication installed in 15 Mobile communication installed in 15 vehiclesvehicles
GPS installed in 5 vehiclesGPS installed in 5 vehicles some 100.000 customers in the Oslo some 100.000 customers in the Oslo
regionregion
goal: dynamic fleet management systemgoal: dynamic fleet management system
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The Pirelli (Cables) CaseThe Pirelli (Cables) Case
Logistics network simulatorLogistics network simulator Assessment of logistical performanceAssessment of logistical performance Detailed analysis of alternative structural Detailed analysis of alternative structural
changeschanges scenarios 6 months operation, 10.000 ordersscenarios 6 months operation, 10.000 orders
GreenTrip - GGT GreenTrip - GGT Systems ArchitectureSystems Architecture
GISGISRoad dataRoad data
Application ServerApplication Server
Application-Application-ModellingModelling
ApplicationApplicationModelModel
Legacy Legacy SystemsSystems
VRP Solver
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The VRP Solver - ObjectsThe VRP Solver - Objects
PlansPlans LocationsLocations VisitsVisits VehiclesVehicles RoutesRoutes DimensionsDimensions ConstraintsConstraints
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VRP Solver - AlgorithmsVRP Solver - Algorithms
ConstructionConstruction– SavingsSavings– SweepSweep– Nearest ...Nearest ...
Improvement, move Improvement, move operatorsoperators– 2-opt, Or-opt2-opt, Or-opt– RelocateRelocate– ExchangeExchange– CrossCross
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VRP Solver - Search ControlVRP Solver - Search Control
Basic heuristicBasic heuristic– Greedy Search (First Improvement)Greedy Search (First Improvement)– Steepest Descent (Best Steepest Descent (Best
Improvement)Improvement) Meta-heuristicsMeta-heuristics
– Tabu SearchTabu Search– Guided Local SearchGuided Local Search– Guided Tabu SearchGuided Tabu Search
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GreenTrip - ResultsGreenTrip - Results
VRP Solver -> ILOG DispatcherVRP Solver -> ILOG Dispatcher GGT -> GreenTrip AS “Dynamic planner”GGT -> GreenTrip AS “Dynamic planner” ““best-until-now” results on OR best-until-now” results on OR
benchmarksbenchmarks Industrial Test CasesIndustrial Test Cases PublicationsPublications
– some 20 scientific paperssome 20 scientific papers– reports - “VRP Solving and IIT Survey”reports - “VRP Solving and IIT Survey”
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GreenTrip DisseminationGreenTrip Dissemination
Kilby, Prosser, Shaw: “Guided Local Search for the VRP”, Proc. MIC 97Kilby, Prosser, Shaw: “Guided Local Search for the VRP”, Proc. MIC 97 De Backer, Furnon: “Metaheuristics in Constraint Programming: Experiments De Backer, Furnon: “Metaheuristics in Constraint Programming: Experiments
with Tabu Search on the VRP”, Proc. MIC 97with Tabu Search on the VRP”, Proc. MIC 97 De Backer, Furnon, Kilby, Prosser, Shaw: “Local Search in Constraint De Backer, Furnon, Kilby, Prosser, Shaw: “Local Search in Constraint
Programming: Applications to vehicle routing problems”, CP 97 Scheduling Programming: Applications to vehicle routing problems”, CP 97 Scheduling WorkshopWorkshop
Hasle: “GreenTrip - the Development of a Generic Toolkit for Vehicle Hasle: “GreenTrip - the Development of a Generic Toolkit for Vehicle Routing”, NOAS 97Routing”, NOAS 97
De Backer, Furnon: “Solving vehicle routing problems with Side Constraints De Backer, Furnon: “Solving vehicle routing problems with Side Constraints Using Constraint Programming”, INFORMS 97Using Constraint Programming”, INFORMS 97
De Backer, Furnon: “Modelling pickup and delivery problems in constraint De Backer, Furnon: “Modelling pickup and delivery problems in constraint programming”, INFORMS 98programming”, INFORMS 98
Bouzoubaa, Hasle, Kloster, Prosser: “The GGT: a Generic Toolkit for VRP Bouzoubaa, Hasle, Kloster, Prosser: “The GGT: a Generic Toolkit for VRP Applications and its Modelling Capabilities”, Proc. PACLP 99Applications and its Modelling Capabilities”, Proc. PACLP 99
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GreenTrip PapersGreenTrip Papers De Backer, Furnon, Kilby, Prosser, Shaw: “Solving vehicle routing De Backer, Furnon, Kilby, Prosser, Shaw: “Solving vehicle routing
problems with constraint programming and metaheuristics”, problems with constraint programming and metaheuristics”, Journal Journal of Heuristics, Special Issue on CPof Heuristics, Special Issue on CP
Kilby, Prosser, Shaw: “A comparison of traditional and constraint-Kilby, Prosser, Shaw: “A comparison of traditional and constraint-based heuristic methods on vehicle routing problems with side based heuristic methods on vehicle routing problems with side constraints”, constraints”, Constraints, April 98Constraints, April 98
De Backer, Furnon: “Local Search in Constraint Programming”, in De Backer, Furnon: “Local Search in Constraint Programming”, in META-HEURISTICS: Advances and Trends in Local Search Paradigms META-HEURISTICS: Advances and Trends in Local Search Paradigms for Optimization (Voss, Martello, Osman, Roucairol, 1999)for Optimization (Voss, Martello, Osman, Roucairol, 1999)
Kilby, Prosser, Shaw: “Guided Local Search for the Vehicle Routing Kilby, Prosser, Shaw: “Guided Local Search for the Vehicle Routing problem with Time Windows”, in META-HEURISTICS: Advances and problem with Time Windows”, in META-HEURISTICS: Advances and Trends in Local Search Paradigms for Optimization (Voss, Martello, Trends in Local Search Paradigms for Optimization (Voss, Martello, Osman, Roucairol, 1999)Osman, Roucairol, 1999)
Kilby, Prosser, Shaw: “Dynamic VRPs: A Study of Scenarios” Kilby, Prosser, Shaw: “Dynamic VRPs: A Study of Scenarios” (forthcoming)(forthcoming)
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Vessel Routing - AmmoniaVessel Routing - Ammonia
Norsk Hydro AgriNorsk Hydro Agri Producer - Consumer Harbours (25)Producer - Consumer Harbours (25) Fleet (10)Fleet (10) Strong Inventory ConstraintsStrong Inventory Constraints External TradingExternal Trading Feasible solutionFeasible solution Earlier approach: MIPEarlier approach: MIP Approach taken: Heuristic Sequencing + Approach taken: Heuristic Sequencing +
LPLP
Producing harbours
Consumingharbours
Harbours with stock inventory
External orders(laycans)
Find the routing plan with the lowest cost so that inventory limits are not exceeded
and all external orders included.
QuantityTime-window
HAMMER Problem
Fleet of vessels
Combinatorial solution
Vessel View: Harbour View:
Site
Route for Vessel 1
Route for Vessel 2
H1:
H2:
H3:
H4:
Vessel 1
Vessel 2
Harbour View: Which vessels, and in which sequence, will call at each harbour.
Vessel View: Which harbours, and in which sequence, each vessel will visit.
H5:
H6:
H7:
1
2 3
4
5
6
7
HAMMER - System overview
Initial solver
Feasible solution
Iterativeimprover
Problem data
LP solver
Greedy Propagator
Combinatorial solution
Update
Feasibility check
HAMMER - Working with the HAMMER - Working with the systemsystem
Initialisation of the problemInitialisation of the problem– Harbours, ships, laycans and planning parametersHarbours, ships, laycans and planning parameters
Schedule generationSchedule generation– Initial solver - from scratch or existingInitial solver - from scratch or existing– Iterative improvementIterative improvement
Analysis and user interactionAnalysis and user interaction– plan statistics - slack, unservicedplan statistics - slack, unserviced– manually change planmanually change plan
Lock ship, harbour or time periodLock ship, harbour or time period
Flatberg, Haavardtun, Kloster, Løkketangen. (2000): Combining exact and Heuristic methods for solving a Vessel Routing Problem with inventory constraints and time windows. To appear in Ricerca Operativa, special issue on combined constraint programming and OR techniques
Research Agenda SAM: Research Agenda SAM: VRPVRP
construction heuristicsconstruction heuristics– construct and improveconstruct and improve– restartrestart– greedy + limited backtrackinggreedy + limited backtracking
IIT by local search and meta-heuristicsIIT by local search and meta-heuristics exact methods subproblems / limited exact methods subproblems / limited
problemsproblems hybrid methodshybrid methods dynamic VRPdynamic VRP empirical investigationempirical investigation
Important topics, SAMImportant topics, SAM
configuration of transportation networksconfiguration of transportation networks VRPs and TSPs with side constraints in VRPs and TSPs with side constraints in
road based and maritime transportationroad based and maritime transportation cheapest path problems in large, cheapest path problems in large,
dynamic network topologiesdynamic network topologies
Proposal to Research Council of NorwayProposal to Research Council of Norway
Research Agenda SAM: Research Agenda SAM: Optimisation / CSPOptimisation / CSP
over-constrained problemsover-constrained problems multi-criterion problemsmulti-criterion problems supply-chain coordinationsupply-chain coordination distributed problem solvingdistributed problem solving
Research Agenda: VRPResearch Agenda: VRP rich models, large problemsrich models, large problems dynamic VRPsdynamic VRPs exact methods for limited (sub)problemsexact methods for limited (sub)problems over-constrained problemsover-constrained problems multi-criteria problemsmulti-criteria problems methodology: problem type - algorithmmethodology: problem type - algorithm cooperating VRP solvers, hybrid methodscooperating VRP solvers, hybrid methods decompositiondecomposition
Issues in Dynamic Fleet Issues in Dynamic Fleet ManagementManagement
Talk atTalk at
ROUTE 2000 - INTERNATIONAL WORKSHOP ROUTE 2000 - INTERNATIONAL WORKSHOP ON VEHICLE ROUTINGON VEHICLE ROUTING
SKODSBORG, DENMARK - AUGUST 16-19, 2000SKODSBORG, DENMARK - AUGUST 16-19, 2000
Geir HasleGeir Hasle
Research Director, Department of Research Director, Department of OptimizationOptimization
SINTEF Applied MathematicsSINTEF Applied Mathematics
Oslo, NorwayOslo, [email protected]@math.sintef.no
http://www.oslo.sintef.no/am/http://www.oslo.sintef.no/am/