Joint Quay Crane and Automated Guided Vehicle Scheduling Optimization in Automated Container Terminals Considering Spare Battery Constraints

With the expansion of automated container terminals (ACTs), joint scheduling among multiple types of equipment has become a critical factor affecting operational efficiency. This study investigates a joint scheduling optimization problem of quay cranes (QCs) and automated guided vehicles (AGVs) by considering AGV battery swapping strategies under spare battery constraints. With the objective of minimizing the final task completion time of AGVs, a mixed-integer programming model is formulated that simultaneously accounts for task assignment, operation sequencing, battery swapping thresholds, spare battery quantity, and mutual waiting times between AGVs and QCs. To solve this problem efficiently, a hill-climbing genetic algorithm (HC-GA) is proposed. Numerical experiments under different task scales show that HC-GA outperforms the genetic algorithm (GA), simulated annealing (SA), Q-learning, and the Q-learning-based genetic algorithm (Q-GA) in key indicators. In addition, the experimental results show that a proper configuration of AGVs can improve scheduling coordination and enhance the energy utilization efficiency of AGVs. The number of spare batteries and the threshold have significant impacts on overall system performance. When both operational efficiency and equipment utilization are considered, appropriately configuring the number of spare batteries and the threshold can effectively enhance the operational efficiency of ACTs.