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The value of personalized dispatch in O2O on-demand delivery services

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

Abstract

In online-to-offline (O2O) on-demand services, customers place orders online and the O2O platform delivers products from stores to customers within a prescribed time. The platform usually hires crowd-sourced drivers as a cost-effective option owing to their flexibility. However, the delivery speed and delivery capacity of the crowd-sourced drivers vary considerably. This service inconsistency brings challenges in precisely matching the delivery supply and customer demand, which may significantly decrease the delivery efficiency. This study aims to address the challenges by proposing a personalized dispatch model, which integrates the order and driver’s characteristics in the order assignment and routing decisions. To achieve this objective, two machine learning-based models are proposed to forecast the delivery speed of individual drivers in real time and customize their delivery capacity dynamically to develop a portrait of each driver’s behaviour. Next, a personalized O2O order assignment and routing model is proposed with the integration of the two aforementioned models. We validate our model with a real dataset of one mainstream O2O platform in China. We run a comprehensive simulation to show the improvement in terms of on-time ratio and average delay time brought by the personalization of each characteristic, namely, delivery speed and delivery capacity. We then show that the proposed personalized model can reduce the average delay by 21.60% through comparison with actual routing decisions by the drivers,. The theoretical and numerical results shed light on the delivery management of the O2O on-demand services.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:304:y:2023:i:3:p:1022-1035
    DOI: 10.1016/j.ejor.2022.05.019
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    as
    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Schuijbroek, J. & Hampshire, R.C. & van Hoeve, W.-J., 2017. "Inventory rebalancing and vehicle routing in bike sharing systems," European Journal of Operational Research, Elsevier, vol. 257(3), pages 992-1004.
    3. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    4. Jiaru Bai & Kut C. So & Christopher S. Tang & Xiqun (Michael) Chen & Hai Wang, 2019. "Coordinating Supply and Demand on an On-Demand Service Platform with Impatient Customers," Manufacturing & Service Operations Management, INFORMS, vol. 21(3), pages 556-570, July.
    5. Stacy A. Voccia & Ann Melissa Campbell & Barrett W. Thomas, 2019. "The Same-Day Delivery Problem for Online Purchases," Service Science, INFORMS, vol. 53(1), pages 167-184, February.
    6. Alnaggar, Aliaa & Gzara, Fatma & Bookbinder, James H., 2021. "Crowdsourced delivery: A review of platforms and academic literature," Omega, Elsevier, vol. 98(C).
    7. Cordeau, Jean-François & Laporte, Gilbert, 2003. "A tabu search heuristic for the static multi-vehicle dial-a-ride problem," Transportation Research Part B: Methodological, Elsevier, vol. 37(6), pages 579-594, July.
    8. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    9. 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.
    10. H. Neil Geismar & Yiwei Huang & Suresh D. Pillai & Chelliah Sriskandarajah & Seokjun Youn, 2020. "Location‐Routing with Conflicting Objectives: Coordinating eBeam Phytosanitary Treatment and Distribution of Mexican Import Commodities," Production and Operations Management, Production and Operations Management Society, vol. 29(6), pages 1506-1531, June.
    11. John Gunnar Carlsson & Erick Delage, 2013. "Robust Partitioning for Stochastic Multivehicle Routing," Operations Research, INFORMS, vol. 61(3), pages 727-744, June.
    12. Markus Ettl & Pavithra Harsha & Anna Papush & Georgia Perakis, 2020. "A Data-Driven Approach to Personalized Bundle Pricing and Recommendation," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 461-480, May.
    13. Iman Dayarian & Martin Savelsbergh, 2020. "Crowdshipping and Same‐day Delivery: Employing In‐store Customers to Deliver Online Orders," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2153-2174, September.
    14. Jean-François Cordeau, 2006. "A Branch-and-Cut Algorithm for the Dial-a-Ride Problem," Operations Research, INFORMS, vol. 54(3), pages 573-586, June.
    15. Krista J. Li, 2018. "Behavior-Based Pricing in Marketing Channels," Marketing Science, INFORMS, vol. 37(2), pages 310-326, March.
    16. Liu, Shan & Jiang, Hai & Chen, Shuiping & Ye, Jing & He, Renqing & Sun, Zhizhao, 2020. "Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    17. Kris Johnson Ferreira & Bin Hong Alex Lee & David Simchi-Levi, 2016. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 69-88, February.
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