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

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.