Automated Guided Vehicle (AGV) Scheduling in Automated Container Terminals (ACTs) Focusing on Battery Swapping and Speed Control

Because they are environmentally friendly and safe, automated guided vehicles (AGVs) are increasingly used in newly constructed automated container terminals. However, their scheduling strategy is constrained by their limited battery capacity. When batteries reach their charging threshold, the AGVs need to be returned to battery-swapping stations. Moreover, the movement speed of AGVs has a significant impact on their energy consumption and operating times. Therefore, in this paper, a speed control strategy that considers the traffic environment of the terminal is proposed from the perspective of energy conservation and emission reduction. In addition, the charging capacity of the terminal is discretized to model its limited handling capacity to avoid congestion in the battery-swapping stations. To minimize the costs of delays and carbon emissions of AGV operations, a mixed integer programming model is established. It optimizes the efficiency and carbon emissions of the operations by assigning and prioritizing container transportation and AGV battery-swapping tasks. An improved genetic algorithm-based approach is designed where a better initial solution is obtained through a greedy strategy, while simulated annealing is adopted for population selection to prevent the algorithm from falling into local optima. Furthermore, an adaptive adjustment strategy for crossover and mutation probabilities is adopted to improve the algorithm's convergence. Finally, a series of numerical experiments is conducted to verify the efficiency of the proposed method. The experimental results indicate that considering the variability of AGV speed can more accurately characterize their energy consumption, and increasing the number of AGVs and enhancing the battery-swapping capacity can effectively reduce the costs of delays and carbon emissions.