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A deep reinforcement learning-based approach for the home delivery and installation routing problem

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  • Qiu, Huaxin
  • Wang, Sutong
  • Yin, Yunqiang
  • Wang, Dujuan
  • Wang, Yanzhang

Abstract

This paper investigates a home delivery and installation routing problem with synchronization constraints stemming from a home industry company in China who provides the last-mile delivery of home decoration and furniture. The company first arranges for products to be delivered from door to door, and later the technicians come to perform the installation service for the customers. The products for each customer must be firstly delivered to the customer by a vehicle and then installed by technicians. The objective is to identify the optimal delivery routes of the vehicles and optimal service routes of the technicians so as to minimize the total travel distance of the delivery and service routes. A deep reinforcement learning method in an Encoder-Decoder fashion with multi-head attention mechanism and beam search strategy is developed to solve the problem. To evaluate the designed method, extensive numerical experiments based on real service networks provided by the company are conducted. The results show that the proposed method can effectively solve the problem, which outperforms some classical strategies, and some meaningful management implications are provided.

Suggested Citation

  • Qiu, Huaxin & Wang, Sutong & Yin, Yunqiang & Wang, Dujuan & Wang, Yanzhang, 2022. "A deep reinforcement learning-based approach for the home delivery and installation routing problem," International Journal of Production Economics, Elsevier, vol. 244(C).
    • Handle: RePEc:eee:proeco:v:244:y:2022:i:c:s0925527321003388
    DOI: 10.1016/j.ijpe.2021.108362
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    References listed on IDEAS

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    1. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
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    4. Ali, Ousmane & Côté, Jean-François & Coelho, Leandro C., 2021. "Models and algorithms for the delivery and installation routing problem," European Journal of Operational Research, Elsevier, vol. 291(1), pages 162-177.
    5. Paraskevopoulos, Dimitris C. & Laporte, Gilbert & Repoussis, Panagiotis P. & Tarantilis, Christos D., 2017. "Resource constrained routing and scheduling: Review and research prospects," European Journal of Operational Research, Elsevier, vol. 263(3), pages 737-754.
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    Cited by:

    1. Jung, Seung Hwan & Yang, Yunsi, 2023. "On the value of operational flexibility in the trailer shipment and assignment problem: Data-driven approaches and reinforcement learning," International Journal of Production Economics, Elsevier, vol. 264(C).
    2. Dey, Bikash Koli & Sarkar, Mitali & Chaudhuri, Kripasindhu & Sarkar, Biswajit, 2023. "Do you think that the home delivery is good for retailing?," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    3. Hashemi-Petroodi, S. Ehsan & Thevenin, Simon & Kovalev, Sergey & Dolgui, Alexandre, 2023. "Markov decision process for multi-manned mixed-model assembly lines with walking workers," International Journal of Production Economics, Elsevier, vol. 255(C).
    4. Zheng, Zhijun & Li, Gang & Cheng, T.C.E & Wu, Feng, 2022. "Offline supplementary service strategies for the online marketplace: Third-party service or marketplace service?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).

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