Fuzzy multi-objective truck scheduling in multi-modal rail–road Physical Internet hubs

The Physical Internet (PI) is an innovative concept that has the potential to significantly improve the efficiency, cost-effectiveness, and sustainability of the global supply chain industry, particularly in cross-docking operations. This paper addresses the truck-scheduling problem in rail–road PI-...

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Vydané v:Computers & industrial engineering Ročník 182; s. 109404
Hlavní autori: Essghaier, Fatma, Chargui, Tarik, Hsu, Tiente, Bekrar, Abdelghani, Allaoui, Hamid, Trentesaux, Damien, Goncalves, Gilles
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.08.2023
Elsevier
Predmet:
Chance constrained programming
Civil Engineering
Cross-docking
Engineering Sciences
Fuzzy theory
Infrastructures de transport
Multi-objective truck scheduling
Physical internet
ε-constraint
Multi-objective truck scheduling
ε-constraint
Physical internet
Cross-docking
Chance constrained programming
Fuzzy theory
ISSN:0360-8352, 1879-0550
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Shrnutí:The Physical Internet (PI) is an innovative concept that has the potential to significantly improve the efficiency, cost-effectiveness, and sustainability of the global supply chain industry, particularly in cross-docking operations. This paper addresses the truck-scheduling problem in rail–road PI-Hubs, taking into account simultaneously both uncertainty and multi-objective decision-making, which has not been fully explored in the literature, particularly for PI-structures. Our proposed approach defines a Multi-Objective Mixed-Integer Programming model (FMO-MIP) that incorporates fuzzy chance-constrained programming and ϵ-constraint to minimize both the total delay and the sum of PI-containers traveled distances, while considering the uncertainty on truck arrival times. This work takes into account the particularities of the Physical Internet and presents a novel decision-making solution to generate a robust Pareto front that aligns with decision-makers’ attitudes towards risk (optimistic/pessimistic) while balancing trade-offs between conflicting objectives. •Multi-objective truck-scheduling in road-rail Physical Internet hub is studied.•A Fuzzy Multi-Objective Mixed-Integer model (FMO-MIP) is developed.•Chance-constrained programming and ϵ-constraint methods are developed.•The FMO-MIP model generates a Pareto optimal solution considering uncertainty.•Managerial insights with an industrial partner are presented.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2023.109404