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A hybrid adaptive large neighbourhood search for multi-depot open vehicle routing problems

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  • Rahma Lahyani
  • Anne-Lise Gouguenheim
  • Leandro C. Coelho

Abstract

In this paper we address the multi-depot open vehicle routing problem (MDOVRP), a complex and difficult problem arising in several real-life applications. In the MDOVRP vehicles start from several depots and do not need to return to the depot at the end of their routes. We propose a hybrid adaptive large neighbourhood search algorithm to solve the MDOVRP coupled with improvement procedures yielding a hybrid metaheuristic. The performance of the proposed metaheuristic is assessed on various benchmark instances proposed for this problem and its special cases, containing up to 48 customers (single-depot version) and up to six depots and 288 customers. The computational results indicate that the proposed algorithm is very competitive compared with the state-of-the-art methods and improves 15 best-known solutions for multi-depot instances and one best-known solution for a single-depot instance. A detailed sensitivity analysis highlights which components of the metaheuristic contribute most to the solution quality.

Suggested Citation

  • Rahma Lahyani & Anne-Lise Gouguenheim & Leandro C. Coelho, 2019. "A hybrid adaptive large neighbourhood search for multi-depot open vehicle routing problems," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 6963-6976, November.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:22:p:6963-6976
    DOI: 10.1080/00207543.2019.1572929
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    Cited by:

    1. Alcaraz, Juan J. & Caballero-Arnaldos, Luis & Vales-Alonso, Javier, 2019. "Rich vehicle routing problem with last-mile outsourcing decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 263-286.
    2. Zhen, Lu & Baldacci, Roberto & Tan, Zheyi & Wang, Shuaian & Lyu, Junyan, 2022. "Scheduling heterogeneous delivery tasks on a mixed logistics platform," European Journal of Operational Research, Elsevier, vol. 298(2), pages 680-698.

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