optimisation modelling in agriculture · presentation phd in industrial engineering. professor. in...

RUC-APS YEAR 4 HIGHLIGHTSMME Alemany Díaz ([email protected])

Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for

Agriculture Production Systems (RUC-APS)

WP8 LeaderMME Alemany Dí[email protected]

Work-Package 8:Agri-Food Supply Chain Decision-Making under Uncertainty

OPTIMISATION MODELLING IN AGRICULTURE


Presentation

PhD in Industrial Engineering

Professor in Operations & Supply Chain Management

Department of Business Organisation

Research Center on Production Management and Engineering Responsible of the Research Unit“Sales & Operations Planning”

Polytechnic University of Valencia Spain

Dr. MarevaAlemany Díaz

Researcher ID: J-2194-2015ORCID: 0000-0002-0992-8441


Presentation

RUC-APS Enhancing and implementing Knowledge based ICT solutions within highRisk and Uncertain Conditions for Agriculture Production Systems

WP8 Agri-food Supply Chain Decision-Making under Uncertainty

WP8 Leader: Dr. Mareva Alemany Díaz

Research Center on Production Management and Engineering


Presentation

1st Position at the UPV in Research Activity per Member along different years

CIGIP is a research structure ofthe Polytechnic University ofValencia, member of thePolytechnic City of Innovation(CPI), the greatest exponent ofthe flow of knowledge andtechnology between researchinstitutions and industry


Presentation

Our staff is multidisciplinary withdifferent university degrees

Raúl Poler(CIGIP-Director)

ÁngelOrtiz

M. Mar Eva Alemany

(WP8 Leader)

JosefaMula

Juan José Pérez

Andrés Boza

Mª JoséVerdecho

Llanos Cuenca

David Pérez

FaustinoAlarcón

Pedro Gómez

Vicente S. Fuertes

CIGIP-UPV TEAM for RUC-APS

Manuel Díaz-Madroñero

Raquel Sanchís

Ana Esteso(ESR)

M. Ángeles(ESR)

Pilar I. Vidal


Contents• Introduction: Contextualization and Current Situation• WP8: Agri-food Supply Chain Decision-Making under Uncertainty• Decision-Making Optimization• Agri-food SC Optimization Problems in WP8• Crop Planning Problem

• Optimization Model to Support Sustainable Crop Planning for Reducing Unfairness among Farmers

• Agri-food SC Design Problem• Optimization Model to Support Sustainable Crop Planning for Reducing Unfairness among

Farmers• Crop Prices Forecasting

• Durum wheat price forecasting• Conclusions


Introduction: Contextualization and Current Situation

Agri-food Supply ChainSet of activities necessary to bring agricultural

products “from the farm to the fork”

Seed Producers Farmers Transporters Processors Distributors Sales Points Consumers


Supply Chain ManagementIntegrated planning, implementation, coordination and control of all business

processes and activities necessary to produce and deliver, as efficiently as possible, products that satisfy market requirements.



Products’ CharacteristicsPerishabilityNon homogeneity

Agri-Food Supply Chain Management

Governmental RegulationsOn Food Safety

ConsumersHigh variation on tastes and preferences

Operational ConstraintsStorage, Processing & Distribution

Very different from that of industrial SCs

UncertaintyProductProcessEnvironmentMarket

v v



Consumer Needs Business Challenges Enabling Technologies

CURRENT SITUATION



WP8 Agri-food SC Decision-

Making under Uncertainty

Data Right Decisions?



WP8: Agri-food SC Decision-Making under UncertaintyAims of WP8 inside RUC-APS Project

Boosting the SUSTAINABILITY of the AGRI-FOOD VALUE CHAINS by

OPTIMIZING its DECISION-MAKINGprocesses in an uncertain context


Decision-Making Optimization: UncertaintyA decision is the conclusion of a process designed to weigh the relative uses or utilities of a set of alternatives on hand, so that decision maker selects the best alternative which is best to his problem or situation and implement it.

Decision Making involves all activities and thinking that are necessary to identify the most optimal or preferred choice among the available alternatives

Decision based on degree of certainty

Rama Murty (2007)

Decisions may be classified depending on the degree of certainty situation:

(i) Decision making under Certainty(ii) Decision making under Uncertainty (iii) Decision making under Risk.

The first two are two extremes and the third one falls between these two with certain probability distribution


Decision-Making Optimization: OR Methods

What is Operational Research?

OR is the discipline of applying advanced analytical methods to help make better decisions.

By using diverse techniques such as problem structuring methods and mathematical modelling to analyse complex situations, OR helps people make more effective decisions based on:

•More complete data•Consideration of all available options•Careful predictions of outcomes and estimates of risk•The latest decision tools and techniques

DECISION-MAKING OPTIMIZATION

Boosting the SUSTAINABILITY of the AGRI-FOOD VALUE CHAINS by

OPTIMIZING its DECISION-MAKINGprocesses in an uncertain context


AFSC SUSTAINABILITY

ENVIRONMENTAL ECONOMIC SOCIAL Profitability

Cost OptimizationEfficiency

Pricing on quality

GHG EmissionEnergy Consumption

Ecological & Resource IssuesWastes

Food SafetyFairness

Employment/TrainingFood needs satisfaction

APPLICABLE MODELS & METHODS Lyfe Cycle Assessment

Linear Programming Non-Linear Programming

Mixed-Integer Programming Multi-Objective Programming

Stochastic Programming

Fuzzy Programming

Simmulation


MATHEMATICAL PROGRAMMING



Known (a posteriori, once the model is solved)


Decision-Making Optimization: OR MethodsExample: CROP PLANNING PROBLEM DESCRIPTION


• Each land area can only be planted once • Everything that is planted is sold

Decision-Making Optimization: OR MethodsASSUMPTIONS

OBJECTIVE FUNCTION

𝑀𝑀𝑀𝑀𝑀𝑀[𝑍𝑍] = �𝑝𝑝𝑖𝑖 ∙ 𝑦𝑦𝑖𝑖 ∙ 𝑋𝑋𝑖𝑖

𝐼𝐼

𝑖𝑖=1

= �𝑝𝑝𝑖𝑖 ∙ 𝑦𝑦𝑖𝑖 ∙ 𝑋𝑋𝑖𝑖𝑖𝑖

CONSTRAINTS - Available land area limit

�𝑋𝑋𝑖𝑖 ≤𝐼𝐼

𝑖𝑖=1

𝑀𝑀

- Available labour resources limit

�𝑡𝑡𝑝𝑝𝑖𝑖 ∙ 𝑋𝑋𝑖𝑖 ≤𝐼𝐼

𝑖𝑖=1

𝑀𝑀𝑡𝑡𝑝𝑝

- Water limitation

�𝑤𝑤𝑖𝑖 ∙ 𝑋𝑋𝑖𝑖

𝐼𝐼

𝑖𝑖=1

≤ 𝑀𝑀𝑤𝑤

- Fertilizers limitation

�𝑓𝑓𝑖𝑖 ∙ 𝑋𝑋𝑖𝑖 ≤𝐼𝐼

𝑖𝑖=1

𝑀𝑀𝑓𝑓

- Non-negativity constratins 𝑋𝑋𝑖𝑖 ≥ 0 ∀𝑖𝑖

- Seeds limitation 𝑠𝑠𝑖𝑖 ∙ 𝑋𝑋𝑖𝑖 ≤ 𝑀𝑀𝑠𝑠𝑖𝑖 ∀𝑖𝑖

DEFINITION PART MODELLING PARTINDEXES i crops (i=1..I) PARAMETERS 𝑡𝑡𝑝𝑝𝑖𝑖 labour time needed to plant one hectare of the crop i 𝑤𝑤𝑖𝑖 water needed per hectare of crop i 𝑓𝑓𝑖𝑖 fertilizers needed per hectare of the crop i 𝑠𝑠𝑖𝑖 seeds needed per hectare of the crop i 𝑝𝑝𝑖𝑖 benefit of the crop i per hectare a available planting area 𝑀𝑀𝑡𝑡𝑝𝑝 availability of labour 𝑀𝑀𝑤𝑤 availability of water 𝑀𝑀𝑓𝑓 availability of fertilizers 𝑀𝑀𝑠𝑠𝑖𝑖 availability of seeds 𝑦𝑦𝑖𝑖 crop yield DECISION VARIABLES 𝑋𝑋𝑖𝑖 area allocated to crop i

(Kg/ha)

(€/Kg)

(ha)


DEMAND PLANNINGFORECASTING

DEMAND FULFILLMENTORDER PROMISING

Agri-food SC Optimization Problems in WP8

Fuente: http://www.revistainternos.com.ar/2018/05/mar-del-plata-descartan-tomates-por-exceso-de-oferta-y-escaso-consumo/Fecha último acceso (13/08/2019)

http://www.revistainternos.com.ar/2018/05/mar-del-plata-descartan-tomates-por-exceso-de-oferta-y-escaso-consumo/


Limited & Uncertain Shelf-lifeConstraints storage possibilities

Uncertain SupplyDue to weather uncertainty it is not known exactly when and how much is going to be harvested

Lack of Collaboration among FarmersThey independently decide the quantity to be planted for each crop, not being possible to know the global supply

Market Prices FluctuationsPrices affect consumers and farmers in an opposite way

Common Farmers’ Practicesproduce more of the previous season's most beneficial crop

Market Demand: Unknown & UncertainAbsence of market demand forecasting and their uncertainty

Imbalance between Supply & Demand


24

Crop PlanningProblem

It refers to the selection of cropsto be planted, the land área allocated to each one and theirdistribution in the farm land

Dury et al (2012)

ALLOCATION RESOURCES PROBLEM MULTIPLE

SOURCES OF UNCERTAINTY

MATHEMATICAL PROGRAMMING

MODELS


Crop Planning Problem: Literature Review Conclusions

SustainabilityEcon+Soc+Med (16 %)

Collaboration & FairnessIt is necessary to address collaboration aspects, especially between farmers at the same stageCentralized models originates maximum unfairness

Policies to minimize riskMininum & Maximum Land Area per Crop

Perishability and Waste95% assumes that all harvested is sold not existing wasteWaste is a FAO’s priority

Planting period & Harvesting18% consider planting period5% include harvesting decisions

Market Demand 70% do not consider the market demand, being the whole production is sold


Crop Planning Problem: Objectives

• Improve the position of power of farmers in the food value chain• through the development of tools for mutually beneficial cooperation,• which allow integrating the needs of producers and consumers, • contributing to the sustainability and a fair distribution of gains between farmers

OBJECTIVES


Optimization Model to Support Sustainable Crop Planning for Reducing Unfairness among Farmers

Actual trend in fresh agri-food sector is to operatewith shorter AFSC with proximity of sales, thereduction of intermediaries and use of transport.Benefits:

• Reduction in the Operation & Inventory costs of fresh products at the retailer

• Sales proximity improves the local social & economic benefits

We propose a novel multi-objective model to support the sustainable perishable crop planning of an AFSC operating with this new business model considering collaboration

among farmers and accounting objectives aligned to sustainability aspects

This business model assumes that all products received at a retailer in one period needs to be sold:

Implications: • More exact demand forecasts are needed• Create mechanisms to sell the excess of supply

in order to minimize wastes• Settlement of products

Current trend in fresh agri-food SCs



• Each farmer: one farming location with an available plantingarea.

• Farmers decide on the area to plant with each crop per period.• Due to technical reasons, a minimum area needs to be planted

per crop and period when it is decided to do so.• The yield per crop plant depends on its planting and harvest

date.• The product has to be harvested when it is mature so no

product can be left on the plant.• Products need to be transported to retailers in the same

period of their harvest.• Harvested products not transported, are wasted at the farming

location.

• Trucks are used to transport products from farming locations to retailers. To use one truck a minimum percentage of the cargocapacity needs to be loaded.

• Products transported to retailers are sold or wasted in the same period due to the high perishability of crops. If there is an excess ofsupply, wastes can be reduced by looking for new demand for settled products limited by a demand percentage.

• A minimum level service is ensured when meeting the demand of each crop.



Objective Functions



�� 𝐴𝐴𝑙𝑙𝑙𝑙𝑝𝑝𝑝𝑝∈𝑃𝑃𝑙𝑙𝑙𝑙

≤ 𝑀𝑀𝑝𝑝𝑙𝑙 ∀𝑙𝑙

𝑀𝑀𝑎𝑎𝑖𝑖𝑛𝑛𝑙𝑙 · 𝑌𝑌𝑃𝑃𝑙𝑙𝑙𝑙𝑝𝑝 ≤ 𝐴𝐴𝑙𝑙𝑙𝑙𝑝𝑝 ≤ 𝑀𝑀𝑝𝑝𝑙𝑙 · 𝑌𝑌𝑃𝑃𝑙𝑙𝑙𝑙𝑝𝑝 ∀𝑙𝑙, 𝑙𝑙, 𝑝𝑝 ∈

𝐻𝐻𝑙𝑙𝑙𝑙𝑡𝑡 = � 𝑦𝑦𝑙𝑙𝑝𝑝𝑡𝑡𝑝𝑝∈𝑃𝑃𝑙𝑙

· 𝐴𝐴𝑙𝑙𝑙𝑙𝑝𝑝 ∀𝑙𝑙, 𝑙𝑙, 𝑡𝑡

𝐻𝐻𝑙𝑙𝑙𝑙𝑡𝑡 = 𝑊𝑊𝑊𝑊𝑙𝑙𝑙𝑙𝑡𝑡 + 𝑇𝑇𝑙𝑙𝑙𝑙𝑡𝑡 ∀𝑙𝑙, 𝑙𝑙, 𝑡𝑡

𝑙𝑙𝑀𝑀𝑝𝑝 · 𝑎𝑎𝑙𝑙 · 𝑁𝑁𝑙𝑙𝑡𝑡 ≤�𝑇𝑇𝑙𝑙𝑙𝑙𝑡𝑡𝑙𝑙

≤ 𝑙𝑙𝑀𝑀𝑝𝑝 · 𝑁𝑁𝑙𝑙𝑡𝑡 ∀𝑙𝑙, 𝑡𝑡

�𝑇𝑇𝑙𝑙𝑙𝑙𝑡𝑡𝑙𝑙

= 𝑆𝑆𝑙𝑙𝑡𝑡 + 𝐺𝐺𝑙𝑙𝑡𝑡 + 𝑊𝑊𝑙𝑙𝑡𝑡 ∀𝑙𝑙, 𝑡𝑡

𝑆𝑆𝑙𝑙𝑡𝑡 + 𝐵𝐵𝑙𝑙𝑡𝑡 = 𝑑𝑑𝑙𝑙𝑡𝑡 ∀𝑙𝑙, 𝑡𝑡 𝐺𝐺𝑙𝑙𝑡𝑡 ≤ 𝑒𝑒𝑙𝑙𝑡𝑡 · 𝑑𝑑𝑙𝑙𝑡𝑡 · 𝑌𝑌𝑙𝑙𝑡𝑡 ∀𝑙𝑙, 𝑡𝑡 𝐵𝐵𝑙𝑙𝑡𝑡 ≤ 𝑑𝑑𝑙𝑙𝑡𝑡 · 𝑌𝑌𝑙𝑙𝑡𝑡 ∀𝑙𝑙, 𝑡𝑡

�𝑆𝑆𝑙𝑙𝑡𝑡𝑡𝑡

≥�𝑠𝑠𝑙𝑙𝑙𝑙 · 𝑑𝑑𝑙𝑙𝑡𝑡𝑡𝑡

∀𝑙𝑙

𝐴𝐴𝑙𝑙𝑙𝑙𝑝𝑝 ,𝐻𝐻𝑙𝑙𝑙𝑙𝑡𝑡 ,𝑊𝑊𝑊𝑊𝑙𝑙𝑙𝑙𝑡𝑡 ,𝑇𝑇𝑙𝑙𝑙𝑙𝑡𝑡 ,𝑊𝑊𝑙𝑙𝑡𝑡 , 𝑆𝑆𝑙𝑙𝑡𝑡 ,𝐵𝐵𝑙𝑙𝑡𝑡 ,𝐺𝐺𝑙𝑙𝑡𝑡 ,𝐷𝐷𝑙𝑙 ,𝑃𝑃𝑃𝑃,𝑃𝑃𝑊𝑊𝑙𝑙 𝑁𝑁𝑙𝑙𝑡𝑡 𝑌𝑌𝑃𝑃𝑙𝑙𝑙𝑙𝑝𝑝

CONSTRAINTS

Limited Total Planting Area

Min & Max Planted Area per Crop

Quantities to be harvested

Transport decisions & wastes’ farmers calculation

Limited capacity of trucks

Calculation of quantity sold, settled, notserved and wasted at retailers

Nature of decisión variables



Case Study of La Plata: Argentina

PLANTING & HARVESTING CALENDAR

• 3 planting seasons• Harvesting periods depend

on planting period



Results

𝟏𝟏𝟏𝟏 𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒𝐒: 𝒘𝒘𝑬𝑬𝑬𝑬 − 𝒘𝒘𝑬𝑬𝑬𝑬𝑬𝑬 − 𝒘𝒘𝑺𝑺𝑺𝑺𝑬𝑬

ECONOMIC

ENVIRONMENTAL

SOCIAL

Computational Efficiency

The multi-objective model counts with • 4,852 constraints, • 90 binary variables, • 520 integer variables, and• 5,415 continuous variables.

MODEL SIZE

Optimal solutions have been found for all Scenarios with a mean resolution time of 2 minutes and 46 seconds.

SOLUTION QUALITY & TIME

1-0-0

0-1-0

0-0-1



UNCERTAINTY MODELLING: Fuzzy Sets

Uncertain parameters: • demand• sales price• settlement prices• crop yields

Characteristics: • Historic data availability and its distribution

probability is hard to obtain• Fuzzy Sets has demonstrated its utility in lack

of information, vagueness and inaccuracy

Fuzzy Sets

Methodology:

Jiménez et al. (2007) to transform a fuzzy model into an equivalent crisp model

𝛼𝛼

��(1 − 𝛼𝛼)𝐸𝐸2𝑀𝑀𝑖𝑖𝑖𝑖 + 𝛼𝛼𝐸𝐸1

𝑀𝑀𝑖𝑖𝑖𝑖 � 𝑀𝑀𝑖𝑖

𝑛𝑛

𝑖𝑖=1

≥ 𝛼𝛼𝐸𝐸2𝑏𝑏𝑖𝑖 + (1 − 𝛼𝛼)𝐸𝐸1

𝑏𝑏𝑖𝑖 𝑖𝑖 = 1,2, … ,𝑎𝑎1

��(1 − 𝛼𝛼)𝐸𝐸1𝑀𝑀𝑖𝑖𝑖𝑖 + 𝛼𝛼𝐸𝐸2

𝑀𝑀𝑖𝑖𝑖𝑖 � 𝑀𝑀𝑖𝑖

𝑛𝑛

𝑖𝑖=1

≤ 𝛼𝛼𝐸𝐸1𝑏𝑏𝑖𝑖 + (1 − 𝛼𝛼)𝐸𝐸2

𝑏𝑏𝑖𝑖 𝑖𝑖 = 𝑎𝑎1 + 1, … ,𝑎𝑎2

��1 −𝛼𝛼2�𝐸𝐸1

𝑀𝑀𝑖𝑖𝑖𝑖 +𝛼𝛼2𝐸𝐸2𝑀𝑀𝑖𝑖𝑖𝑖 � 𝑀𝑀𝑖𝑖

𝑛𝑛

𝑖𝑖=1

≤𝛼𝛼2𝐸𝐸1𝑏𝑏𝑖𝑖 + �1 −

𝛼𝛼2�𝐸𝐸2


��1 −𝛼𝛼2�𝐸𝐸2

𝑀𝑀𝑖𝑖𝑖𝑖 +𝛼𝛼2𝐸𝐸1𝑀𝑀𝑖𝑖𝑖𝑖 � 𝑀𝑀𝑖𝑖

𝑛𝑛

𝑖𝑖=1

≥𝛼𝛼2𝐸𝐸2𝑏𝑏𝑖𝑖 + �1 −

𝛼𝛼2�𝐸𝐸1


𝐸𝐸𝐼𝐼(�̃�𝑧) = [𝐸𝐸1𝑧𝑧 ,𝐸𝐸2

𝑧𝑧] = �𝑧𝑧1 + 𝑧𝑧2

2,𝑧𝑧3 + 𝑧𝑧4

2�


Agri-food Supply Chain DesignAFSC design is shape the nature of the supply chain (Melnik et al., 2013) being the possible decisions (Chopra and Meindl, 2007): Facility location, Facility role, Capacity allocation, Relationship between stakeholders

Relevance of supply chain configuration on agri-food Sector:

• Supply chain configuration limits the possible future tactical and operational decisions to be made (Melniket al., 2013).

• Operative aspects of agri-food sectors (e.g. limited shelf-life) impact on strategic decisions.

• Agri-food supply chain should be designed by combining strategic decision-making with the operative characteristics.


Agri-food Supply Chain Design: perishability impact

Challenge: To determine the impact that products’ perishability has on the agri-food AFSC design

Problem characteristics• Design of the entire SC: Farm, packing plant, warehouses and DC stages.• Tactical-Operative decisions: Planting, cultivation, harvest, laboring, packing, transport, storage.• Multiple products with limited shelf life.• Minimum shelf life (freshness) is required at markets.• Minimum planting areas per crop due to technical reasons.• Different harvest patterns.• Limited availability of resources:

• workforce capacity in farms: Plant, cultivate and harvest activities.• packing capacity at packing plant.• storage & management capacity at warehouses & DCs

• Objective: maximization of supply chain profits.

RUC-APS YEAR 4 HIGHLIGHTSMME Alemany Díaz ([email protected])38

Agri-food Supply Chain Design: perishability impact

…………………

…………………

MIXED INTEGER LINEAR PROGRAMMING MODEL


Agri-Food Supply Chain Design: perishability impactExperimental Results

Packing plant Distributioncentre

MarketFarm Warehouse

t

520

• Realistic data from La Plata (Argentine)• 3 crops

• Horizon:• 52 periods

• Locations: • 10 farms• 8 packing plants• 4 warehouses• 8 distribution Centers

• Scenarios: • Variability in product shelf-life• Five Scenarios:

• SL=1 period• SL=2 periods• SL=3 periods • SL=4 periods• SL=5 periods


Agri-Food Supply Chain Design: perishability impactExperimental Results: Managerial Insights

Managerial Insights• Different SC configuration for products

with different shelf-life.

• From a certain shelf-life, perishability of products does not impact on the SC configuration.

• Positive impact on profits, customer service level, and wastes when increasing product shelf-life:

• Important to invest in technology to:• Obtain products with longer shelf-

life• Extend product’

Farms Packing Plants Ware-houses

DistributionCenters


Agri-Food Supply Chain Design: perishability impact

Experimental Results: Computational Efficiency

Shelf-life Continuousvariables

Integervariables

Binaryvariables Constraints Resolution

time GAP

1 31,254 19,210 210 190,432 0.00 hours -

2 51,054 19,210 210 190,432 0.02 hours -

3 71,354 19,210 210 190,432 0.65 hours -

4 88,854 19,210 210 190,432 8.25 hours -

5 106,854 19,210 210 190,432 24 hours 0.32%

Resolution time limited to 24 hours


Computer Tools used for MPM OptimizationOptimization Architecture

MATHEMATICAL MODEL

DATA STORAGE

MPL MODELLING LANGUAGE

SOLVER

INPUT PARAMETERS

OPTIMUM VALUE OBJECTIVE & DECISION

VARIABLES


Durum wheat Price forecasting


Conclusions

DIGITAL TECHNOLOGIES

MATHEMATICAL MODELS

REDUCED USE BY FARMERS

HARDWARE PROCESSING CAPACITY

SOFTWARE PACKAGES

MATH MODELS UPDATE

Improve the Data Availabilityto feed MathematicalProgramaming Models

License are free only for academicsand expensive forpractitioners

Big Problems require highprocessing capacity to be solved in time

Have proved their validtityfor achieving agri-foodsustainability

MPM are consideredhard to develop , use and understand

Maintenaince of MM require the expertknowledge

DIRECTOR Professor Raúl Poler “Agri-food Supply Chain Optimisation Service” is a very easy and friendly optimization tool in the cloud for optimising problems of different nature. No optimization background is necessary. You can choose between already existing models or ask for the development of a new model sending an e-mail to [email protected]

Don’t forget to attend to the Demonstrator Session about this Optimization ServiceWed, May 20, 2020 4:00 PM - 5:00 PM (CEST)

Please join CIGIP Director’s meeting from your computer, tablet or smartphone. https://global.gotomeeting.com/join/197516573


RUC-APS WP8: Some Publications • Esteso A., Alemany M.M.E., Ortiz A. (2017) Conceptual Framework for Managing Uncertainty in a Collaborative Agri-Food Supply Chain Context. In:

Camarinha-Matos L., Afsarmanesh H., Fornasiero R. (eds) Collaboration in a Data-Rich World. PRO-VE 2017. IFIP Advances in Information and Communication Technology, vol 506. Springer, Cham.

• Esteso A., M.M.E. Alemany, Ortiz A. (2018): Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models, International Journal of Production Research, DOI: 10.1080/00207543.2018.1447706

• Esteso A., M.M.E. Alemany, Ortiz A. Guyon, C. (2018) A Collaborative Model to Improve Farmers' Skill Level by Investments in an Uncertain Context. IFIP Advances in Information and Communication Technology (534)590 - 598.

• H. Grillo, M.M.E. Alemany, A. Ortiz & B. De Baets (2019): Possibilistic compositions and state functions: application to the order promising process for perishables, International Journal of Production Research.

• Isabel Mundi, M. M. E. Alemany, Raúl Poler & Vicente S. Fuertes-Miquel (2019): Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model, International Journal of Production Research

• Zhao G. Shaofeng Liu, Huilan Chen, Carmen Lopez, Jorge Hernandez, Cécile Guyon, Rina Iannacone, Nicola Calabrese, Hervé Panetto, Janusz Kacprzyk, MME Alemany. (2019) Value-Chain Wide Food Waste Management: A Systematic Literature Review. In: Freitas P., Dargam F., Moreno J. (eds) Decision Support Systems IX: Main Developments and Future Trends. EmC-ICDSST 2019. Lecture Notes in Business Information Processing, vol 348. Springer, Cham

• Zaraté P., Alemany M., del Pino M., Alvarez A.E., Camilleri G. (2019) How to Support Group Decision Making in Horticulture: An Approach Based on the Combination of a Centralized Mathematical Model and a Group Decision Support System. In: Freitas P., Dargam F., Moreno J. (eds) Decision Support Systems IX: Main Developments and Future Trends. EmC-ICDSST 2019. Lecture Notes in Business Information Processing, vol 348. Springer, Cham.


RUC-APS WP8: Some Publications • Mario Lezoche, Jorge E. Hernandez, Maria del Mar Eva Alemany Diaz,Hervé Panetto, Janusz Kacprzy (2020),,Agri-food 4.0: A survey

of the supply chains and technologies for the future agriculture, Computers in Industry, Volume 117, 103-187

• Alejandra Garrido, Leandro Antonelli, Jonathan Martin, M.M.E. Alemany, Josefa Mula, (2020), Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem, Computers and Electronics in Agriculture 170 (2020) 105242

• Raquel Sanchis, Luca Canetta and Raúl Poler, 2020, A Conceptual Reference Framework for Enterprise Resilience Enhancement Sustainability, 12, 1464; doi:10.3390/su12041464

• Ortiz A., Alarcón F., Pérez D., Alemany M.M.E. (2019) Identifying the Main Uncertainties in the Agri-Food Supply Chain. In: Mula J., Barbastefano R., Díaz-Madroñero M., Poler R. (eds) New Global Perspectives on Industrial Engineering and Management. Lecture Notes in Management and Industrial Engineering. Springer, Cham

• Pérez Perales D., Alarcón Valero F., Drummond C., Ortiz Á. (2019) Towards a Sustainable Agri-food Supply Chain Model. The Case of LEAF. In: Ortiz Á., Andrés Romano C., Poler R., García-Sabater JP. (eds) Engineering Digital Transformation. Lecture Notes in Management and Industrial Engineering. Springer, Cham

• Verdecho MJ., Alfaro-Saiz JJ., Rodríguez-Rodríguez R. (2019) Integrating Business Process Interoperability into an Inter-enterprise Performance Management System. In: Popplewell K., Thoben KD., Knothe T., Poler R. (eds) Enterprise Interoperability VIII. Proceedings of the I-ESA Conferences, vol 9. Springer, Cham


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