Integrated scheduling optimization of U-shaped automated container terminal under loading and unloading mode

This paper proposes an integrated scheduling optimization model based on mixed integer programming to analytically characterize the U-shaped automated container terminal layout and handling technology. We focus on dual trolley quay cranes, conflict-free automated guided vehicles (AGVs) and dual cantilever rail cranes under loading and unloading mode, which have rarely been simultaneously studied in the literature, as most prior research has addressed traditional container terminals. We eliminate the waiting time during the interaction between AGV and dual cantilever rail crane to realize spatiotemporal synchronization and minimize the completion time of all tasks. We employ a reinforcement learning based hyper-heuristic genetic algorithm to solve the model, specifically, better solution results for reward and punishment mechanism incorporating reinforcement learning, higher versatility independent of specific problems, stronger scalability of low-level algorithms. We investigate which algorithm is better by comparing the proposed algorithm with bi-level genetic algorithm, adaptive genetic algorithm, hybrid genetic algorithm and cuckoo search algorithm. We conduct small-sized and large-sized experiments to validate the performance of the proposed model and algorithm. The results show that the proposed model and algorithm can not only avoid the conflicts among AGVs but also significantly improve handling efficiency.