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Provably High-Quality Solutions for the Meal Delivery Routing Problem

Author

Listed:
  • Baris Yildiz

    (Department of Industrial Engineering, Koc University, 34450 Istanbul, Turkey)

  • Martin Savelsbergh

    (H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

Abstract
Online restaurant aggregators with integrated meal delivery networks have become more common and more popular in the past few years. Meal delivery is arguably the ultimate challenge in last-mile logistics: a typical order is expected to be delivered within an hour (much less if possible) and within minutes of the food becoming ready. We introduce a novel formulation for a meal delivery routing problem (in which we assume perfect information about order arrivals) and develop a simultaneous column- and row-generation method for its solution. The analysis of the results of an extensive computational study, using instances derived from real-life data, demonstrates the efficacy of the solution approach, and provides valuable insights into, among others, the (potential) benefits of order bundling, courier-shift scheduling, and demand management.

Suggested Citation

  • Baris Yildiz & Martin Savelsbergh, 2019. "Provably High-Quality Solutions for the Meal Delivery Routing Problem," Transportation Science, INFORMS, vol. 53(5), pages 1372-1388, September.
    • Handle: RePEc:inm:ortrsc:v:53:y:2019:i:5:p:1372-1388
    DOI: 10.1287/trsc.2018.0887
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    References listed on IDEAS

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    5. Liu, Yang & Li, Sen, 2023. "An economic analysis of on-demand food delivery platforms: Impacts of regulations and integration with ride-sourcing platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    6. Hao, Peng & Liu, Haishan & Liao, Yejia & Boriboonsomsin, Kanok & Barth, Matthew J, 2022. "Developing Environmentally Friendly Solutions for On-Demand Food Delivery Service," Institute of Transportation Studies, Working Paper Series qt89c461pv, Institute of Transportation Studies, UC Davis.
    7. Long He & Sheng Liu & Zuo‐Jun Max Shen, 2022. "Smart urban transport and logistics: A business analytics perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3771-3787, October.
    8. 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).
    9. Yildiz, Baris & Savelsbergh, Martin, 2020. "Pricing for delivery time flexibility," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 230-256.
    10. Ye, Anke & Zhang, Kenan & Chen, Xiqun (Michael) & Bell, Michael G.H. & Lee, Der-Horng & Hu, Simon, 2024. "Modeling and managing an on-demand meal delivery system with order bundling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 187(C).
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