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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| Published in: | Computers & industrial engineering Vol. 182; p. 109404 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.08.2023
Elsevier |
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| ISSN: | 0360-8352, 1879-0550 |
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| Abstract | 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. |
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| AbstractList | 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 -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. 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. |
| ArticleNumber | 109404 |
| Author | Allaoui, Hamid Essghaier, Fatma Chargui, Tarik Bekrar, Abdelghani Hsu, Tiente Trentesaux, Damien Goncalves, Gilles |
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| Keywords | Multi-objective truck scheduling ε-constraint Physical internet Cross-docking Chance constrained programming Fuzzy theory |
| Language | English |
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