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Integrating crowd-shipping into last-mile delivery: A two-stage stochastic optimization approach

Author

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  • Tang, Shixuan
  • Huang, Wentao
  • Jian, Sisi

Abstract

Crowd-shipping has emerged as a cost-effective solution to meet the growing demand for urban last-mile delivery. This paper examines the integration of partial crowd-shipping into traditional parcel delivery from the perspective of a delivery company. We explicitly consider both crowd-shipping supply and parcel demand uncertainties, and formulate a parcel delivery problem with crowd-shipping in stochastic programming (PDPCS-SP) that aims to minimize the expected total operational costs. Given the computational complexity of the problem, we develop a heuristic algorithm based on an adaptive large neighborhood search (ALNS) to efficiently solve large-scale instances. Extensive numerical experiments demonstrate the effectiveness and efficiency of the proposed PDPCS-SP model and solution approach, highlighting the potential to enhance the sustainability and economic viability of last-mile delivery operations.

Suggested Citation

  • Tang, Shixuan & Huang, Wentao & Jian, Sisi, 2025. "Integrating crowd-shipping into last-mile delivery: A two-stage stochastic optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:transe:v:203:y:2025:i:c:s1366554525003837
    DOI: 10.1016/j.tre.2025.104342
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