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

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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.

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Acknowledgements

The authors wish to express their gratitude to the esteemed reviewers for their valuable comments in improving the quality of this research work.

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Authors and Affiliations

  1. Department of Industrial Management, University of Tehran, Tehran, Iran

    Iman Ghasemian Sahebi & Seyed Pendar Toufighi

  2. Department of Technology and Innovation, University of Southern Denmark, Odense, Denmark

    Seyed Pendar Toufighi

  3. Department of Industrial Management, University of Qom, Qom, Iran

    Mahdi Azzavi

  4. Department of Industrial Engineering, University of Yazd, Yazd, Iran

    Faezeh Zare

Authors
  1. Iman Ghasemian Sahebi
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  2. Seyed Pendar Toufighi
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  3. Mahdi Azzavi
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  4. Faezeh Zare
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Contributions

Conceptualization: IGS; methodology: SPT, formal analysis: IGS and SPT; investigation: MA and FZ; writing—original draft preparation: SPT; writing—review and editing: MA and FZ; supervision: IGS. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Seyed Pendar Toufighi.

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Sahebi, I.G., Toufighi, S.P., Azzavi, M. et al. Presenting an optimization model for multi cross-docking rescheduling location problem with metaheuristic algorithms. OPSEARCH 61, 137–162 (2024). https://doi.org/10.1007/s12597-023-00694-5

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