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New variable depth local search for multiple depot vehicle scheduling problems

Author

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  • Tomoshi Otsuki

    (Toshiba Corporation
    The University of Tokyo)

  • Kazuyuki Aihara

    (The University of Tokyo)

Abstract

The multiple depot vehicle scheduling problem (MDVSP) is a well-known and important NP-hard problem in transport scheduling. In this study, we first provide an original interpretation of the search space of the MDVSP. Next, we present a local search algorithm which utilizes pruning and deepening techniques in the variable depth search framework. Computational results using well-known test cases show that our method achieves better results than the second-best local search based method does by 8.6–30.1 %, and exhibits the best short-term performance among the state-of-the-art methods.

Suggested Citation

  • Tomoshi Otsuki & Kazuyuki Aihara, 2016. "New variable depth local search for multiple depot vehicle scheduling problems," Journal of Heuristics, Springer, vol. 22(4), pages 567-585, August.
    • Handle: RePEc:spr:joheur:v:22:y:2016:i:4:d:10.1007_s10732-014-9264-z
    DOI: 10.1007/s10732-014-9264-z
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    References listed on IDEAS

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    Cited by:

    1. Guedes, Pablo C. & Borenstein, Denis, 2018. "Real-time multi-depot vehicle type rescheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 217-234.
    2. Kulkarni, Sarang & Krishnamoorthy, Mohan & Ranade, Abhiram & Ernst, Andreas T. & Patil, Rahul, 2018. "A new formulation and a column generation-based heuristic for the multiple depot vehicle scheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 457-487.

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