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Dispatch optimisation in O2O on-demand service with crowd-sourced and in-house drivers

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  • Jiawei Tao
  • Hongyan Dai
  • Hai Jiang
  • Weiwei Chen

Abstract

O2O (Online to Offline) services enable customers to place orders online and receive products/services offline. In addition to traditional in-house drivers, the emergence of crowd-sourced drivers provides an opportunity to re-organise offline delivery services. In practice, three types of workforce, namely, in-house, full-time, and part-time crowd-sourced drivers, coexist in the system while exhibiting different characteristics. This situation creates challenges for the management of order assignment and routing. In particular, we study three settings in response to different driver preferences: the guaranteed minimum daily number of orders for full-time drivers; the maximally allowed number of orders per trip; and the detour proportion for part-time drivers. This paper aims to provide a method for O2O platforms to optimise order assignment and routing, considering these designs about driver preferences. We further validate our model and study managerial insights using real datasets. Specifically, the results show that among all designed parameters for the O2O on-demand delivery system, two parameters – the maximally allowed number of orders per trip and the detour proportion – are critical for the design. Moreover, we find that incentive mechanisms for inexperienced and experienced drivers are different because of their service capacities. The managerial insights are expected to guide practitioners.

Suggested Citation

  • Jiawei Tao & Hongyan Dai & Hai Jiang & Weiwei Chen, 2021. "Dispatch optimisation in O2O on-demand service with crowd-sourced and in-house drivers," International Journal of Production Research, Taylor & Francis Journals, vol. 59(20), pages 6054-6068, October.
    • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:20:p:6054-6068
    DOI: 10.1080/00207543.2020.1800120
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    Cited by:

    1. Sun, Yanru & Sun, Hao & Sun, Panfei & Jin, Xuanzhu, 2024. "A dynamic surge pricing model throughout product lifecycle," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 226(C), pages 139-151.
    2. Xiao, Haohan & Xu, Min & Wang, Shuaian, 2023. "A game-theoretic model for crowd-shipping operations with profit improvement strategies," International Journal of Production Economics, Elsevier, vol. 262(C).
    3. Sun, Xuting & Fang, Minghao & Guo, Shu & Hu, Yue, 2024. "UAV-rider coordinated dispatching for the on-demand delivery service provider," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    4. Tao, Jiawei & Dai, Hongyan & Chen, Weiwei & Jiang, Hai, 2023. "The value of personalized dispatch in O2O on-demand delivery services," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1022-1035.
    5. Yang, Dingtong & Hyland, Michael F. & Jayakrishnan, R., 2024. "Tackling the crowdsourced shared-trip delivery problem at scale with a novel decomposition heuristic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).

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