Optimizing AGV Battery Swapping for Sustainable Port Efficiency

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.