Multi-objective genetic-based algorithms for a cross-docking scheduling problem

The importance and applicability of cross-docking systems have grown rapidly in recent years. As these systems play a key role, particularly, in distribution networks, the launch of multi-objective approaches can contribute to solve the real-world cases and problems of such systems, in which many different and even conflicting objectives are considered. Hence, this paper addresses three famous multi-objective algorithms including non-dominated sorting genetic algorithm-II (NSGA-II), strength Pareto evolutionary algorithm-II (SPEA-II), and sub-population genetic algorithm-II (SPGA-II) to solve the cross-docking scheduling problem, in which product items are unloaded from inbound trailers in the receiving dock and then are categorized and loaded onto outbound trailers in the shipping dock. Since the time aspect of such activities is so determining and crucial, objective functions are considered as the total operational time (makespan) and the total lateness of all outbound trailers. Furthermore, In order to appraise the performance of these algorithms, four criteria are proposed and compared with each other to demonstrate the strengths of each applied algorithm.