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A parallel multi-neighborhood cooperative tabu search for capacitated vehicle routing problems

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  • Jin, Jianyong
  • Crainic, Teodor Gabriel
  • Løkketangen, Arne

Abstract

This paper presents a parallel tabu search algorithm that utilizes several different neighborhood structures for solving the capacitated vehicle routing problem. Single neighborhood or neighborhood combinations are encapsulated in tabu search threads and they cooperate through a solution pool for the purpose of exploiting their joint power. The computational experiments on 32 large scale benchmark instances show that the proposed method is highly effective and competitive, providing new best solutions to four instances while the average deviation of all best solutions found from the collective best results reported in the literature is about 0.22%. We are also able to associate the beneficial use of special neighborhoods with some test instance characteristics and uncover some sources of the collective power of multi-neighborhood cooperation.

Suggested Citation

  • Jin, Jianyong & Crainic, Teodor Gabriel & Løkketangen, Arne, 2012. "A parallel multi-neighborhood cooperative tabu search for capacitated vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 222(3), pages 441-451.
    • Handle: RePEc:eee:ejores:v:222:y:2012:i:3:p:441-451
    DOI: 10.1016/j.ejor.2012.05.025
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Goeke, Dominik, 2019. "Granular tabu search for the pickup and delivery problem with time windows and electric vehicles," European Journal of Operational Research, Elsevier, vol. 278(3), pages 821-836.
    2. Schneider, Michael & Schwahn, Fabian & Vigo, Daniele, 2017. "Designing granular solution methods for routing problems with time windows," European Journal of Operational Research, Elsevier, vol. 263(2), pages 493-509.
    3. Aderemi Oluyinka Adewumi & Olawale Joshua Adeleke, 2018. "A survey of recent advances in vehicle routing problems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 155-172, February.
    4. Lei, Deming & Guo, Xiuping, 2015. "A parallel neighborhood search for order acceptance and scheduling in flow shop environment," International Journal of Production Economics, Elsevier, vol. 165(C), pages 12-18.
    5. Michael Schneider & Maximilian Löffler, 2019. "Large Composite Neighborhoods for the Capacitated Location-Routing Problem," Service Science, INFORMS, vol. 53(1), pages 301-318, February.
    6. Abbas Tarhini & Kassem Danach & Antoine Harfouche, 2022. "Swarm intelligence-based hyper-heuristic for the vehicle routing problem with prioritized customers," Annals of Operations Research, Springer, vol. 308(1), pages 549-570, January.
    7. 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.
    8. Hemmelmayr, Vera C., 2015. "Sequential and parallel large neighborhood search algorithms for the periodic location routing problem," European Journal of Operational Research, Elsevier, vol. 243(1), pages 52-60.
    9. Schryen, Guido, 2020. "Parallel computational optimization in operations research: A new integrative framework, literature review and research directions," European Journal of Operational Research, Elsevier, vol. 287(1), pages 1-18.

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