Hybrid Particle Swarm Optimization for Two-stage Cross Docking Scheduling

Abstract

In order to improve supply chain's operation efficiency, shorten delivery time and decrease distribution costs, two-stage cross docking scheduling problem under direct shipment mode was studied in this paper. Taking into consideration the influence of numbers of vehicles in distribution center on cross docking problem, three models were established based on different assumptions including only one vehicle in distribution center, many vehicles in distribution center and the location of distribution center to be determined, and the objective was to minimize transportation time. Hybrid particle swarm optimization was proposed to solve the model on the basis of PSO and GA. The algorithm introduced clone selection operator to make particles multiply and mutate by calculating the affinity between individuals so that the best individual can be reserved and the poor can be improved. Clone operator, crossover operator, antibody reorganization operator and mutation operator were designed to improve the performance of the algorithm. Computational experiments showed that the hybrid particle swarm optimization algorithm has faster convergence speed and better solution precision compared with other algorithms. The result of the present work implied that the model in this paper was accord with the reality, and it was effective and feasible.