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.

Driven by low-carbon and environmental sustainability initiatives, automated container terminals (ACTs) increasingly employ automated guided vehicles (AGVs) as horizontal transportation equipment. As AGVs are electrically powered, battery swapping operations can affect their continuous operational capability. To address the concentration of battery-swapping demand and the resulting queuing phenomenon during AGV operations at ACTs, this study proposes a probability-based staggered battery-swapping strategy. When an AGV’s battery level falls within the candidate swapping range, the AGV performs battery swapping in advance with a certain probability, thereby shifting part of the swapping demand from the original concentrated period to earlier periods. A scheduling optimization model is established with the objective of minimizing the maximum completion time of AGVs, and a genetic algorithm (GA) tailored to the characteristics of the model is developed for solution. The performance of the proposed GA is compared with those of a standard GA and simulated annealing, and the effectiveness of the proposed strategy is further evaluated through a small-scale exact solution and numerical experiments under different battery-swapping strategies. The results show that the proposed GA achieves good solution quality and stability under different AGV configurations and can closely approach the exact optimal solution for the small-scale instance. Compared with the original battery-swapping strategy, the staggered battery-swapping strategy effectively disperses AGV battery-swapping arrival times, reduces queuing at the battery-swapping station, and shortens the maximum completion time of the system. An appropriate setting of the staggered swapping probability can further improve the temporal distribution of swapping demand. Robustness analysis shows that the proposed strategy maintains good performance under the different operating conditions considered in this study.