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Feasibility of crowdshipping for outlier parcels in last-mile delivery

Author

Listed:
  • Cebeci, Merve Seher
  • de Bok, Michiel
  • Tapia, Rodrigo
  • Nadi, Ali
  • Tavasszy, Lóránt

Abstract

As the rapid growth of urban e-commerce increases the volume of last-mile deliveries, logistics service providers have difficulty in meeting the demand of on-demand consumer requests. This increase in demand challenges traditional delivery, with some parcels becoming disproportionately costly to deliver to their destinations. To address this, we introduce a cost-based outlier parcel selection mechanism that identifies parcels with a high negative impact on the marginal delivery costs. These outlier parcels are then eliminated from their tours and outsourced to a crowdshipping market, where individuals combine the delivery task with their already planned trips. We use unique data on delivery tours of six service providers for the province of South Holland in the Netherlands. The cost-based decision rule for identifying outlier parcels results in a low proportion of outsourcing to the crowdshipping market compared to earlier literature. We identify only about 1 % of the total parcel demand as outliers across all carriers combined. Of these outlier parcels, the proportion selected for crowdshipping based on their cost efficiency ranges from 42.78 % to 3 %, depending on the scenario. While crowdshipping provides a viable solution for handling a small portion of last-mile deliveries, its environmental and economic sustainability is restricted by factors such as compensation rates and the delivery mode used. This study demonstrates that outsourcing high-cost outlier parcels to crowdshipping can be cost-efficient and reduce emissions of last-mile logistics companies; however, the proportion of these parcels is very small, limiting the overall impact on sustainability.

Suggested Citation

  • Cebeci, Merve Seher & de Bok, Michiel & Tapia, Rodrigo & Nadi, Ali & Tavasszy, Lóránt, 2025. "Feasibility of crowdshipping for outlier parcels in last-mile delivery," Research in Transportation Economics, Elsevier, vol. 112(C).
  • Handle: RePEc:eee:retrec:v:112:y:2025:i:c:s0739885925000903
    DOI: 10.1016/j.retrec.2025.101607
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    References listed on IDEAS

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    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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