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An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem

Author

Listed:
  • Li, Xiangyong
  • Tian, Peng
  • Aneja, Y.P.

Abstract

This paper studies the heterogeneous fixed fleet vehicle routing problem (HFFVRP), in which the fleet is composed of a fixed number of vehicles with different capacities, fixed costs, and variable costs. Given the fleet composition, the HFFVRP is to determine a vehicle scheduling strategy with the objective of minimizing the total transportation cost. We propose a multistart adaptive memory programming (MAMP) and path relinking algorithm to solve this problem. Through the search memory, MAMP at each iteration constructs multiple provisional solutions, which are further improved by a modified tabu search. As an intensification strategy, path relinking is integrated to enhance the performance of MAMP. We conduct a series of experiments to evaluate and demonstrate the effectiveness of the proposed algorithm.

Suggested Citation

  • Li, Xiangyong & Tian, Peng & Aneja, Y.P., 2010. "An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(6), pages 1111-1127, November.
    • Handle: RePEc:eee:transe:v:46:y:2010:i:6:p:1111-1127
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    Citations

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    Cited by:

    1. Lai, David S.W. & Caliskan Demirag, Ozgun & Leung, Janny M.Y., 2016. "A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 86(C), pages 32-52.
    2. Puca Huachi Vaz Penna & Anand Subramanian & Luiz Satoru Ochi & Thibaut Vidal & Christian Prins, 2019. "A hybrid heuristic for a broad class of vehicle routing problems with heterogeneous fleet," Annals of Operations Research, Springer, vol. 273(1), pages 5-74, February.
    3. Joaquín Pacheco & Rafael Caballero & Manuel Laguna & Julián Molina, 2013. "Bi-Objective Bus Routing: An Application to School Buses in Rural Areas," Transportation Science, INFORMS, vol. 47(3), pages 397-411, August.
    4. Li, Xiangyong & Wei, Kai & Aneja, Y.P. & Tian, Peng, 2017. "Design-balanced capacitated multicommodity network design with heterogeneous assets," Omega, Elsevier, vol. 67(C), pages 145-159.
    5. Xiaodan Wu & Ruichang Li & Chao-Hsien Chu & Richard Amoasi & Shan Liu, 2022. "Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach," Annals of Operations Research, Springer, vol. 308(1), pages 653-684, January.
    6. Liu, Shuguang, 2013. "A hybrid population heuristic for the heterogeneous vehicle routing problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 54(C), pages 67-78.
    7. Salhi, Said & Wassan, Niaz & Hajarat, Mutaz, 2013. "The Fleet Size and Mix Vehicle Routing Problem with Backhauls: Formulation and Set Partitioning-based Heuristics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 22-35.
    8. Houda Derbel & Bassem Jarboui & Rim Bhiri, 2019. "A skewed general variable neighborhood search algorithm with fixed threshold for the heterogeneous fleet vehicle routing problem," Annals of Operations Research, Springer, vol. 272(1), pages 243-272, January.
    9. Moshref-Javadi, Mohammad & Lee, Seokcheon & Winkenbach, Matthias, 2020. "Design and evaluation of a multi-trip delivery model with truck and drones," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    10. 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.
    11. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "Thirty years of heterogeneous vehicle routing," European Journal of Operational Research, Elsevier, vol. 249(1), pages 1-21.

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