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Pre-auction optimization for the selection of shared customers in the last-mile delivery

Author

Listed:
  • Amina Antit

    (University of Tunis El Manar)

  • Amel Jaoua

    (University of Tunis El Manar)

  • Safa Bhar Layeb

    (University of Tunis El Manar)

  • Chefi Triki

    (University of Kent)

Abstract

Companies are constantly looking for new strategies to improve their logistics performance and ensure their competitiveness in the global market. This article provides a new scheme for managing the selection of shared customers for a logistics company. The new mechanism proposes the use of the auction as a tool to manage the selection of shared clients through the coalition pool. Thus, all unprofitable shared customers will be pushed to the pool for outsourcing by the other collaborating carriers. Then, some profitable auctioned ones will be selected. The selection system is designed based on solving a vehicle routing problem that aims to maximize the carrier's profit in a decentralized context. At first, a mixed integer linear programing model is derived to solve the deterministic version of the problem. Then in order to efficiently address the stochastic version of the problem, a simulation-based optimization model is developed. This model is employed to solve a real case study of a parcel delivery company, considering the travel times as a bimodal distribution. A comparative study is conducted to demonstrate the effectiveness of the auction approach in managing shared customers. The results of our study reveal that the proposed auction approach efficiently manages the shared customers which leads to the substantial increase of 22.65% in profits for the delivery company. These findings have significant implications for logistics companies seeking to improve their performance and competitiveness in the global market.

Suggested Citation

  • Amina Antit & Amel Jaoua & Safa Bhar Layeb & Chefi Triki, 2025. "Pre-auction optimization for the selection of shared customers in the last-mile delivery," Annals of Operations Research, Springer, vol. 344(2), pages 989-1026, January.
    • Handle: RePEc:spr:annopr:v:344:y:2025:i:2:d:10.1007_s10479-023-05711-w
    DOI: 10.1007/s10479-023-05711-w
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    References listed on IDEAS

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