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A rolling horizon algorithm for auto-carrier transportation

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  • Cordeau, Jean-François
  • Dell’Amico, Mauro
  • Falavigna, Simone
  • Iori, Manuel

Abstract

This paper introduces a rolling horizon algorithm to plan the delivery of vehicles to automotive dealers by a heterogeneous fleet of auto-carriers. The problem consists in scheduling the deliveries over a multiple-day planning horizon during which requests for transportation arrive dynamically. In addition, the routing of the auto-carriers must take into account constraints related to the loading of the vehicles on the carriers. The objective is to minimize the sum of traveled distances, fixed costs for auto-carrier operation, service costs, and penalties for late deliveries. The problem is solved by a heuristic that first selects the vehicles to be delivered in the next few days and then optimizes the deliveries by an iterated local search procedure. A branch-and-bound search is used to check the feasibility of the loading. To handle the dynamic nature of the problem, the complete algorithm is applied repeatedly in a rolling horizon framework. Computational results on data from a major European logistics service provider show that the heuristic is fast and yields significant improvements compared to the sequential solution of independent daily problems.

Suggested Citation

  • Cordeau, Jean-François & Dell’Amico, Mauro & Falavigna, Simone & Iori, Manuel, 2015. "A rolling horizon algorithm for auto-carrier transportation," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 68-80.
    • Handle: RePEc:eee:transb:v:76:y:2015:i:c:p:68-80
    DOI: 10.1016/j.trb.2015.02.009
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    Cited by:

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    5. Wang, Yu & Chen, Feng & Chen, Zhi-Long, 2018. "Pickup and delivery of automobiles from warehouses to dealers," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 412-430.
    6. Anirudh Subramanyam & Frank Mufalli & José M. Lí?nez-Aguirre & Jose M. Pinto & Chrysanthos E. Gounaris, 2021. "Robust Multiperiod Vehicle Routing Under Customer Order Uncertainty," Operations Research, INFORMS, vol. 69(1), pages 30-60, January.
    7. Hu, Wuhua & Mao, Jianfeng & Wei, Keji, 2017. "Energy-efficient rail guided vehicle routing for two-sided loading/unloading automated freight handling system," European Journal of Operational Research, Elsevier, vol. 258(3), pages 943-957.
    8. Chen, Feng & Wang, Yu, 2020. "Downward compatible loading optimization with inter-set cost in automobile outbound logistics," European Journal of Operational Research, Elsevier, vol. 287(1), pages 106-118.
    9. Vélez-Gallego, Mario C. & Teran-Somohano, Alejandro & Smith, Alice E., 2020. "Minimizing late deliveries in a truck loading problem," European Journal of Operational Research, Elsevier, vol. 286(3), pages 919-928.
    10. Zhang, Dong & Klabjan, Diego, 2017. "Optimization for gate re-assignment," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 260-284.
    11. Christian Billing & Florian Jaehn & Thomas Wensing, 2018. "A multiperiod auto-carrier transportation problem with probabilistic future demands," Journal of Business Economics, Springer, vol. 88(7), pages 1009-1028, September.
    12. Carlos A. Vega-Mejía & Jairo R. Montoya-Torres & Sardar M. N. Islam, 2019. "Consideration of triple bottom line objectives for sustainability in the optimization of vehicle routing and loading operations: a systematic literature review," Annals of Operations Research, Springer, vol. 273(1), pages 311-375, February.
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