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Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

Author

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  • Tarik Chargui

    (LAMIH, UMR CNRS 8201, Université Polytechnique Hauts-de-France, Le Mont Houy, 59313 Valenciennes, France
    RSAID, ENSATe, University of Abdelmalek Essaadi, Tétouan 93000, Morocco)

  • Abdelghani Bekrar

    (LAMIH, UMR CNRS 8201, Université Polytechnique Hauts-de-France, Le Mont Houy, 59313 Valenciennes, France)

  • Mohamed Reghioui

    (RSAID, ENSATe, University of Abdelmalek Essaadi, Tétouan 93000, Morocco)

  • Damien Trentesaux

    (LAMIH, UMR CNRS 8201, Université Polytechnique Hauts-de-France, Le Mont Houy, 59313 Valenciennes, France)

Abstract

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

Suggested Citation

  • Tarik Chargui & Abdelghani Bekrar & Mohamed Reghioui & Damien Trentesaux, 2019. "Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption," Sustainability, MDPI, vol. 11(11), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3127-:d:236797
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    References listed on IDEAS

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    Cited by:

    1. Chargui, Tarik & Ladier, Anne-Laure & Bekrar, Abdelghani & Pan, Shenle & Trentesaux, Damien, 2022. "Towards designing and operating physical internet cross-docks: Problem specifications and research perspectives," Omega, Elsevier, vol. 111(C).
    2. Reza Kiani Mavi & Mark Goh & Neda Kiani Mavi & Ferry Jie & Kerry Brown & Sharon Biermann & Ahmad A. Khanfar, 2020. "Cross-Docking: A Systematic Literature Review," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
    3. Abdelghani Bekrar & Abdessamad Ait El Cadi & Raca Todosijevic & Joseph Sarkis, 2021. "Digitalizing the Closing-of-the-Loop for Supply Chains: A Transportation and Blockchain Perspective," Sustainability, MDPI, vol. 13(5), pages 1-25, March.
    4. Cempírek Václav & Rybicka Iwona & Ljubaj Ivica, 2019. "Development of Electromobility in Terms of Freight Transport," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 10(2), pages 23-32, November.
    5. Feifeng Zheng & Yaxin Pang & Yinfeng Xu, 2022. "Heuristics for cross-docking scheduling of truck arrivals, truck departures and shop-floor operations," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1571-1601, July.
    6. Johan M. Bogoya & Andrés Vargas & Oliver Schütze, 2019. "The Averaged Hausdorff Distances in Multi-Objective Optimization: A Review," Mathematics, MDPI, vol. 7(10), pages 1-35, September.
    7. Oluwatosin Theophilus & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Masoud Kavoosi, 2019. "Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review," Sustainability, MDPI, vol. 11(19), pages 1-23, September.

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