Optimizing Last-Mile Delivery with Crowdshipping and Considering Carriers' Acceptance Probability

Authors

  • Majid Bagheri Ghadikolaei * Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran. https://orcid.org/0000-0001-6259-9723
  • Abdollah Aghaei Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.

https://doi.org/10.48313/scodm.v3i4.69

Abstract

The rapid growth of digital businesses in recent years, along with the inherent complexity of Last-Mile Delivery (LMD), has posed significant challenges to urban logistics in terms of cost, traffic congestion, and environmental pollution. This study develops a mathematical model that assigns parcel delivery tasks (via delivery lockers) to Public Transport (PT) passengers and Ride-Hailing Vehicles (RHVs) using a crowdshipping approach. The objective is to optimize both logistics and environmental costs. Because crowdshipping carriers may accept delivery requests with uncertainty, a logit model predicts the probability of accepting a given delivery offer based on parcel characteristics. This probability is incorporated into the objective function of a Mixed-Integer Programming (MIP) formulation as an expected cost of non-acceptance. To estimate the behavioral parameters, a fractional factorial design was used to construct a twelve-scenario stated-preference survey. We collected 200 responses from PT passengers and RVH drivers and used them to calibrate the logit model coefficients. We then solved a numerical example in Python using the PuLP optimization library. The results indicate that PT passengers are more sensitive to financial incentives, while RVHs drivers are more sensitive to parcel volume. We explicitly integrated these differences into the objective function, and the findings show that neglecting carrier behavior can significantly affect decision-making and potentially lead to misleading "optimal" solutions. Overall, the proposed framework enables smarter and more sustainable management of LMD systems.

Keywords:

Last-mile delivery, Delivery lockers, Crowdshipping, Logit model, Urban logistics

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Published

2026-12-01

Issue

Vol. 3 No. 4 (2026): Supply Chain and Operations Decision Making

Section

Articles

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Copyright (c) 2026 Supply Chain and Operations Decision Making

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Bagheri Ghadikolaei, M. ., & Aghaei, A. . (2026). Optimizing Last-Mile Delivery with Crowdshipping and Considering Carriers’ Acceptance Probability. Supply Chain and Operations Decision Making, 3(4), 289-302. https://doi.org/10.48313/scodm.v3i4.69

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