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

Iman Ghasemian Sahebi, Seyed Pendar Toufighi (), Mahdi Azzavi and Faezeh Zare
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Iman Ghasemian Sahebi: University of Tehran
Seyed Pendar Toufighi: University of Tehran
Mahdi Azzavi: University of Qom
Faezeh Zare: University of Yazd

OPSEARCH, 2024, vol. 61, issue 1, No 7, 137-162

Abstract: 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.

Keywords: Rescheduling problem; Truck scheduling; Cross-docking; Meta-heuristic algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12597-023-00694-5

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