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Deep Q-learning for same-day delivery with vehicles and drones

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  • Chen, Xinwei
  • Ulmer, Marlin W.
  • Thomas, Barrett W.

Abstract

In this paper, we consider same-day delivery with vehicles and drones. Customers make delivery requests over the course of the day, and the dispatcher dynamically dispatches vehicles and drones to deliver the goods to customers before their delivery deadline. Vehicles can deliver multiple packages in one route but travel relatively slowly due to the urban traffic. Drones travel faster, but they have limited capacity and require charging or battery swaps. To exploit the different strengths of the fleets, we propose a deep Q-learning approach. Our method learns the value of assigning a new customer to either drones or vehicles as well as the option to not offer service at all. In a systematic computational analysis, we show the superiority of our policy compared to benchmark policies and the effectiveness of our deep Q-learning approach. We also show that the combination of state and action features is very valuable and that our policy can maintain effectiveness when demand data and the fleet size change moderately.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:298:y:2022:i:3:p:939-952
    DOI: 10.1016/j.ejor.2021.06.021
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    References listed on IDEAS

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    Cited by:

    1. Bosse, Alexander & Ulmer, Marlin W. & Manni, Emanuele & Mattfeld, Dirk C., 2023. "Dynamic priority rules for combining on-demand passenger transportation and transportation of goods," European Journal of Operational Research, Elsevier, vol. 309(1), pages 399-408.
    2. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    3. Chen, Xinwei & Wang, Tong & Thomas, Barrett W. & Ulmer, Marlin W., 2023. "Same-day delivery with fair customer service," European Journal of Operational Research, Elsevier, vol. 308(2), pages 738-751.
    4. Liu, Zeyu & Li, Xueping & Khojandi, Anahita, 2022. "The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    5. Banerjee, Dipayan & Erera, Alan L. & Stroh, Alexander M. & Toriello, Alejandro, 2023. "Who has access to e-commerce and when? Time-varying service regions in same-day delivery," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 148-168.
    6. Nikola Mardešić & Tomislav Erdelić & Tonči Carić & Marko Đurasević, 2023. "Review of Stochastic Dynamic Vehicle Routing in the Evolving Urban Logistics Environment," Mathematics, MDPI, vol. 12(1), pages 1-44, December.
    7. Abdul Aziz Khan Niazi & Tehmina Fiaz Qazi & Maryam Aziz & Abdul Basit & Ifra Aziz Khan Niazi, 2023. "Using the Binary Matrices for Modeling the Supply Chain Issues of Virtual Shops: An Evidence from Pakistan," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 9(2), pages 548-564.
    8. 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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