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Dynamic vehicle routing with random requests: A literature review

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  • Zhang, Jian
  • Woensel, Tom Van

Abstract

Stimulated by the growing demand for logistics services and the advances in information technologies, research interest in the dynamic vehicle routing problem with random requests (DVRPRR) increased over recent decades. The DVRPRR differs from classical vehicle routing in that the customer requests are not fully known in advance but arrive dynamically during the execution of planned routes. Various approaches were developed to deal with this dynamism and the potential stochasticity. This paper provides a comprehensive and in-depth review of the existing DVRPRR literature. We propose a novel taxonomy that identifies four DVRPRR variants with different request types and planning horizons. We also analyze the research on each variant from the perspectives of problem settings, decision strategies, solution approaches, etc. Finally, we summarize the state of the art of the DVRPRR and suggest promising directions for future research.

Suggested Citation

  • Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
  • Handle: RePEc:eee:proeco:v:256:y:2023:i:c:s0925527322003334
    DOI: 10.1016/j.ijpe.2022.108751
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    4. Zhang, Jian & Luo, Kelin & Florio, Alexandre M. & Van Woensel, Tom, 2023. "Solving large-scale dynamic vehicle routing problems with stochastic requests," European Journal of Operational Research, Elsevier, vol. 306(2), pages 596-614.
    5. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
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    17. Marlin W. Ulmer & Barrett W. Thomas & Ann Melissa Campbell & Nicholas Woyak, 2021. "The Restaurant Meal Delivery Problem: Dynamic Pickup and Delivery with Deadlines and Random Ready Times," Transportation Science, INFORMS, vol. 55(1), pages 75-100, 1-2.
    18. Ozbaygin, Gizem & Savelsbergh, Martin, 2019. "An iterative re-optimization framework for the dynamic vehicle routing problem with roaming delivery locations," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 207-235.
    19. Marlin W. Ulmer & Leonard Heilig & Stefan Voß, 2017. "On the Value and Challenge of Real-Time Information in Dynamic Dispatching of Service Vehicles," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(3), pages 161-171, June.
    20. Chen, Xinwei & Ulmer, Marlin W. & Thomas, Barrett W., 2022. "Deep Q-learning for same-day delivery with vehicles and drones," European Journal of Operational Research, Elsevier, vol. 298(3), pages 939-952.

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