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On-Demand Delivery from Stores: Dynamic Dispatching and Routing with Random Demand

Author

Listed:
  • Sheng Liu

    (Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada)

  • Zhixing Luo

    (Department of Industrial Engineering and Operations Management, School of Management and Engineering, Nanjing University, Nanjing City, 210093 Jiangsu, China)

Abstract

Problem definition : On-demand delivery has become increasingly popular around the world. Motivated by a large grocery chain store who offers fast on-demand delivery services, we model and solve a stochastic dynamic driver dispatching and routing problem for last-mile delivery systems where on-time performance is the main target. The system operator needs to dispatch a set of drivers and specify their delivery routes facing random demand that arrives over a fixed number of periods. The resulting stochastic dynamic program is challenging to solve because of the curse of dimensionality. Methodology/results : We propose a novel structured approximation framework to approximate the value function via a parametrized dispatching and routing policy. We analyze the structural properties of the approximation framework and establish its performance guarantee under large-demand scenarios. We then develop efficient exact algorithms for the approximation problem based on Benders decomposition and column generation, which deliver verifiably optimal solutions within minutes. Managerial implications : The evaluation results on a real-world data set show that our framework outperforms the current policy of the company by 36.53% on average in terms of delivery time. We also perform several policy experiments to understand the value of dynamic dispatching and routing with varying fleet sizes and dispatch frequencies.

Suggested Citation

  • Sheng Liu & Zhixing Luo, 2023. "On-Demand Delivery from Stores: Dynamic Dispatching and Routing with Random Demand," Manufacturing & Service Operations Management, INFORMS, vol. 25(2), pages 595-612, March.
    • Handle: RePEc:inm:ormsom:v:25:y:2023:i:2:p:595-612
    DOI: 10.1287/msom.2022.1171
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    References listed on IDEAS

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