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Stochastic crowd shipping last-mile delivery with correlated marginals and probabilistic constraints

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  • Silva, Marco
  • Pedroso, João Pedro
  • Viana, Ana

Abstract

In this work, we study last-mile delivery with the option of crowd shipping. A company uses occasional drivers to complement its fleet in the activity of delivering products to its customers. We model it as a variant of the stochastic capacitated vehicle routing problem. Our approach is data-driven, where not only customer orders but also the availability of occasional drivers are uncertain. It is assumed that marginal distributions of the uncertainty vector are known, but the joint distribution is difficult to estimate. We optimize considering a worst-case joint distribution and model with a strategic planning perspective, where we calculate an optimal a priori solution before the uncertainty is revealed. A limit on the infeasibility of the routes due to the capacity is imposed using probabilistic constraints.

Suggested Citation

  • Silva, Marco & Pedroso, João Pedro & Viana, Ana, 2023. "Stochastic crowd shipping last-mile delivery with correlated marginals and probabilistic constraints," European Journal of Operational Research, Elsevier, vol. 307(1), pages 249-265.
    • Handle: RePEc:eee:ejores:v:307:y:2023:i:1:p:249-265
    DOI: 10.1016/j.ejor.2022.10.039
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    Cited by:

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    2. Wang, Yi-Jia & Wang, Yue & Huang, George Q. & Lin, Ciyun, 2024. "Public acceptance of crowdsourced delivery from a customer perspective," European Journal of Operational Research, Elsevier, vol. 317(3), pages 793-805.
    3. 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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