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Implicit depot assignments and rotations in vehicle routing heuristics

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  • Vidal, Thibaut
  • Crainic, Teodor Gabriel
  • Gendreau, Michel
  • Prins, Christian

Abstract

Vehicle routing variants with multiple depots and mixed fleet present intricate combinatorial aspects related to sequencing choices, vehicle type choices, depot choices, and depots positioning. This paper introduces a dynamic programming methodology for efficiently evaluating compound neighborhoods combining sequence-based moves with an optimal choice of vehicle and depot, and an optimal determination of the first customer to be visited in the route, called rotation. The assignment choices, making the richness of the problem, are thus no more addressed in the solution structure, but implicitly determined during each move evaluation. Two meta-heuristics relying on these concepts, an iterated local search and a hybrid genetic algorithm, are presented. Extensive computational experiments demonstrate the remarkable performance of these methods on classic benchmark instances for multi-depot vehicle routing problems with and without fleet mix, as well as the notable contribution of the implicit depot choice and positioning methods to the search performance. New state-of-the-art results are obtained for multi-depot vehicle routing problems (MDVRP), and multi-depot vehicle fleet mix problems (MDVFMP) with unconstrained fleet size. The proposed concepts are fairly general, and widely applicable to many other vehicle routing variants.

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  • Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2014. "Implicit depot assignments and rotations in vehicle routing heuristics," European Journal of Operational Research, Elsevier, vol. 237(1), pages 15-28.
  • Handle: RePEc:eee:ejores:v:237:y:2014:i:1:p:15-28
    DOI: 10.1016/j.ejor.2013.12.044
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    1. Liu, Shuguang & Huang, Weilai & Ma, Huiming, 2009. "An effective genetic algorithm for the fleet size and mix vehicle routing problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(3), pages 434-445, May.
    2. Thibaut Vidal & Teodor Gabriel Crainic & Michel Gendreau & Nadia Lahrichi & Walter Rei, 2012. "A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems," Operations Research, INFORMS, vol. 60(3), pages 611-624, June.
    3. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2013. "Heuristics for multi-attribute vehicle routing problems: A survey and synthesis," European Journal of Operational Research, Elsevier, vol. 231(1), pages 1-21.
    4. Fred Glover & Jin-Kao Hao, 2011. "The case for strategic oscillation," Annals of Operations Research, Springer, vol. 183(1), pages 163-173, March.
    5. Beasley, JE, 1983. "Route first--Cluster second methods for vehicle routing," Omega, Elsevier, vol. 11(4), pages 403-408.
    6. Dondo, Rodolfo & Cerda, Jaime, 2007. "A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1478-1507, February.
    7. Gilbert Laporte, 2009. "Fifty Years of Vehicle Routing," Transportation Science, INFORMS, vol. 43(4), pages 408-416, November.
    8. Paolo Toth & Daniele Vigo, 2003. "The Granular Tabu Search and Its Application to the Vehicle-Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 15(4), pages 333-346, November.
    9. Irnich, Stefan, 2000. "A multi-depot pickup and delivery problem with a single hub and heterogeneous vehicles," European Journal of Operational Research, Elsevier, vol. 122(2), pages 310-328, April.
    10. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2014. "A unified solution framework for multi-attribute vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 234(3), pages 658-673.
    11. Subramanian, Anand & Penna, Puca Huachi Vaz & Uchoa, Eduardo & Ochi, Luiz Satoru, 2012. "A hybrid algorithm for the Heterogeneous Fleet Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 221(2), pages 285-295.
    12. Chris Groër & Bruce Golden & Edward Wasil, 2011. "A Parallel Algorithm for the Vehicle Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 23(2), pages 315-330, May.
    13. Imran, Arif & Salhi, Said & Wassan, Niaz A., 2009. "A variable neighborhood-based heuristic for the heterogeneous fleet vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 197(2), pages 509-518, September.
    14. Goel, Asvin & Gruhn, Volker, 2008. "A General Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 650-660, December.
    15. Salhi, S. & Sari, M., 1997. "A multi-level composite heuristic for the multi-depot vehicle fleet mix problem," European Journal of Operational Research, Elsevier, vol. 103(1), pages 95-112, November.
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    Cited by:

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    2. Escobar, John Willmer & Linfati, Rodrigo & Baldoquin, Maria G. & Toth, Paolo, 2014. "A Granular Variable Tabu Neighborhood Search for the capacitated location-routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 344-356.
    3. Baozhen Yao & Chao Chen & Xiaolin Song & Xiaoli Yang, 2019. "Fresh seafood delivery routing problem using an improved ant colony optimization," Annals of Operations Research, Springer, vol. 273(1), pages 163-186, February.
    4. Schmidt, Carise E. & Silva, Arinei C.L. & Darvish, Maryam & Coelho, Leandro C., 2023. "Time-dependent fleet size and mix multi-depot vehicle routing problem," International Journal of Production Economics, Elsevier, vol. 255(C).
    5. Fatih Kocatürk & G. Yazgı Tütüncü & Said Salhi, 2021. "The multi-depot heterogeneous VRP with backhauls: formulation and a hybrid VNS with GRAMPS meta-heuristic approach," Annals of Operations Research, Springer, vol. 307(1), pages 277-302, December.
    6. Rafael Martinelli & Claudio Contardo, 2015. "Exact and Heuristic Algorithms for Capacitated Vehicle Routing Problems with Quadratic Costs Structure," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 658-676, November.

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