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Data-driven decision and analytics of collection and delivery point location problems for online retailers

Author

Listed:
  • Xianhao Xu

    (EM - EMLyon Business School)

  • Yaohan Shen
  • Wanying (amanda) Chen
  • Yeming Gong
  • Hongwei Wang

Abstract

The location of collection and delivery points (CDPs), impacted by online customers' demand data, plays an important role for online retailers. While previous delivery points optimization researches do not use customer behavior data, we propose new models, integrating with customer behavior data analysis, to optimize collection and delivery points for online retailers. We explore a real customer behavior data and use totally 257,685 users' records (212,062 records for training set and 45,623 records test set). We first estimate the customer purchase probability by five data mining models. Based on the estimation results, we establish two facility location models to respectively optimize the attended and unattended CDPs locations with the objective of cost minimization. Our numerical experiments make a quantitative analysis of customer service level and location cost. Our results can further help online retailers to decide the suitable CDPs with trading off the consumer service level and the total logistics cost. We make interesting contributions: (i) we analyze real customer behavior data and find that gradient boosting trees algorithm outperform other four algorithms when estimating customers' purchase probabilities; (ii) We propose a new data-driven method integrating data mining models and facility location models to determine CDP locations for online retailers.

Suggested Citation

  • Xianhao Xu & Yaohan Shen & Wanying (amanda) Chen & Yeming Gong & Hongwei Wang, 2021. "Data-driven decision and analytics of collection and delivery point location problems for online retailers," Post-Print hal-03188219, HAL.
    • Handle: RePEc:hal:journl:hal-03188219
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    Citations

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    Cited by:

    1. Yang, Tiannuo & Chu, Zhongzhu & Wang, Bailin, 2023. "Feasibility on the integration of passenger and freight transportation in rural areas: A service mode and an optimization model," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    2. Sariyer, Gorkem & Mangla, Sachin Kumar & Sozen, Mert Erkan & Li, Guo & Kazancoglu, Yigit, 2024. "Leveraging explainable artificial intelligence in understanding public transportation usage rates for sustainable development," Omega, Elsevier, vol. 127(C).
    3. Wang, Haibo & Alidaee, Bahram, 2023. "White-glove service delivery: A quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    4. Chen, Yajing & Wu, Zhimin & Wang, Yunlong, 2024. "Omnichannel product selection and shelf space planning optimization," Omega, Elsevier, vol. 127(C).
    5. Li, Leiting & Huang, Min & Yue, Xiaohang & Wang, Xingwei, 2024. "The strategic analysis of collection delivery points network sharing in last-mile logistics market," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    6. Kahr, Michael, 2022. "Determining locations and layouts for parcel lockers to support supply chain viability at the last mile," Omega, Elsevier, vol. 113(C).
    7. Alyahya, Mansour & Agag, Gomaa & Aliedan, Meqbel & Abdelmoety, Ziad H., 2023. "A cross-cultural investigation of the relationship between eco-innovation and customers boycott behaviour," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    8. Iacocca, Kathleen & Mahar, Stephen & Daniel Wright, P., 2022. "Strategic horizontal integration for drug cost reduction in the pharmaceutical supply chain," Omega, Elsevier, vol. 108(C).

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