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Presenting an optimization model for multi cross-docking rescheduling location problem with metaheuristic algorithms

Author

Listed:
  • Iman Ghasemian Sahebi

    (University of Tehran)

  • Seyed Pendar Toufighi

    (University of Tehran
    University of Southern Denmark)

  • Mahdi Azzavi

    (University of Qom)

  • Faezeh Zare

    (University of Yazd)

Abstract

The cross-docking policy has a significant impact on supply chain productivity. This research optimizes the rescheduling location problem for incoming and outgoing trucks in a multi-cross-docking system. Contrary to previous studies, it first considers the simultaneous effects of learning and deteriorating on loading and unloading the jobs. A mixed integer non-linear multi-objective programming model is developed. The truck rescheduling location problem in a cross-docking system is strongly considered an NP-hard problem. Thus, this study uses two meta-heuristic algorithms: multi-objective particle swarm optimization (MOPSO) and non-dominated ranking genetic algorithm (NRGA). Finally, the numerical results obtained from meta-heuristic algorithms are examined using the relative percentage deviation and comparison criteria. The findings demonstrate that MOPSO outperforms NRGA with a 91.1% degree of confidence in all metrics. Also, results show that the NRGA algorithm provides more expansive answers than the MOPSO when measured against the maximum expansion criterion.

Suggested Citation

  • Iman Ghasemian Sahebi & Seyed Pendar Toufighi & Mahdi Azzavi & Faezeh Zare, 2024. "Presenting an optimization model for multi cross-docking rescheduling location problem with metaheuristic algorithms," OPSEARCH, Springer;Operational Research Society of India, vol. 61(1), pages 137-162, March.
    • Handle: RePEc:spr:opsear:v:61:y:2024:i:1:d:10.1007_s12597-023-00694-5
    DOI: 10.1007/s12597-023-00694-5
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