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Efficient Neighborhood Evaluations for the Vehicle Routing Problem with Multiple Time Windows

Author

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  • Maaike Hoogeboom

    (Department of Supply Chain Analytics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands)

  • Wout Dullaert

    (Department of Supply Chain Analytics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands)

  • David Lai

    (Department of Supply Chain Analytics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands)

  • Daniele Vigo

    (Department of Supply Chain Analytics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands; Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi,” University of Bologna, 40136 Bologna, Italy)

Abstract

In the vehicle routing problem with multiple time windows (VRPMTW), a single time window must be selected for each customer from the multiple time windows provided. Compared with classical vehicle routing problems with only a single time window per customer, multiple time windows increase the complexity of the routing problem. To minimize the duration of any given route, we present an exact polynomial time algorithm to efficiently determine the optimal start time for servicing each customer. The proposed algorithm has a reduced worst-case and average complexity than existing exact algorithms. Furthermore, the proposed exact algorithm can be used to efficiently evaluate neighborhood operations during a local search resulting in significant acceleration. To examine the benefits of exact neighborhood evaluations and to solve the VRPMTW, the proposed algorithm is embedded in a simple metaheuristic framework generating numerous new best known solutions at competitive computation times.

Suggested Citation

  • Maaike Hoogeboom & Wout Dullaert & David Lai & Daniele Vigo, 2020. "Efficient Neighborhood Evaluations for the Vehicle Routing Problem with Multiple Time Windows," Transportation Science, INFORMS, vol. 54(2), pages 400-416, March.
    • Handle: RePEc:inm:ortrsc:v:54:y:2020:i:2:p:400-416
    DOI: 10.1287/trsc.2019.0912
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    References listed on IDEAS

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    3. Fei Peng & Xian Fan & Puxin Wang & Mingan Sheng, 2022. "A Time-Space Network-Based Optimization Method for Scheduling Depot Drivers," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
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    5. Tao Yang & Weixin Wang & Qiqi Wu, 2022. "Fuzzy Demand Vehicle Routing Problem with Soft Time Windows," Sustainability, MDPI, vol. 14(9), pages 1-14, May.
    6. Arjun Paul & Ravi Shankar Kumar & Chayanika Rout & Adrijit Goswami, 2021. "A bi-objective two-echelon pollution routing problem with simultaneous pickup and delivery under multiple time windows constraint," OPSEARCH, Springer;Operational Research Society of India, vol. 58(4), pages 962-993, December.

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