Coordinated Scheduling Optimization of Automatic Container Terminal Based On Hybrid Genetic Particle Swarm Optimization Algorithm

In order to reduce the turnaround time of vessels and improve the efficiency of loading and unloading operations at an automated container terminal, this paper proposes a loading and unloading process involving YC-AGV-mate-AGV-QC. The study establishes a mixed-integer programming model with the coordinated scheduling of AGVs, QCs, and YCs, based on the consideration about the constraint on AGV-mate quantity. The model is solved with a hybrid genetic particle swarm optimization algorithm. Experimental results demonstrate that the hybrid genetic particle swarm optimization algorithm outperforms the particle swarm algorithm. Furthermore, at a certain quantity of AGV-mates, the completion time decreases with an increase in the number of AGVs. When the quantity of AGV is optimal, the completion time increases with further increases in AGV quantity.