A Genetic Algorithm Approach for Multi Objective Cross Dock Scheduling in Supply Chains

Abstract In supply chain, cross docking terminal aims to move products directly from inbound trucks to outbound trucks; however, value or non-value added operations might be needed before loading into outbound trucks, for instance, repacking, sorting, and labeling. Also, cross docking terminal might be the place for consolidated shipment intended for full truckload freight or less-than-truckload freight and can reduce the inventory cost of supply chain network. Further, cross docking helps reducing space required and reducing transportation costs. Although the literature shows several methods for improving operations in cross docking terminal, there is a gap in finding the inventory level of temporary storage considering truck scheduling and dock assignment simultaneously. In this paper, Genetic Algorithm (GA) is used for solving the truck scheduling and dock assignment for the inbound and outbound trucks with minimizing three objectives namely makespan, maximum inventory level, and total traveling distance of material handling. Also, the performance of the four crossover strategies is compared in finding the satisfaction level of the three objectives combined. The results show that, among the four crossover strategies, none of the crossover strategies is dominant in finding the best satisfaction level according the dataset used.