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<p>In order to enhance the operational efficiency of Automated Guided Vehicles (AGVs) within a container terminal, particularly in a U-shaped automated container terminal (ACT), a comprehensive analysis of terminal transportation system and storage yard layout is conducted. A dual-threshold charging strategy is proposed, which considers both allowable working power threshold and allowable charging power threshold, and an AGV scheduling model with the goal of minimizing task completion time is established. Meanwhile, a Genetic-ALNS Hybrid Algorithm (GAHA) is used to optimize the AGV scheduling sequence, combining genetic algorithm with adaptive large neighborhood search algorithm. The experimental results show that the GAHA algorithm solves the AGV scheduling problem with a better convergence speed and finds a better optimal solution than the adaptive genetic algorithm combining the greedy strategy and the genetic algorithm. In addition, the dual-threshold charging strategy is compared with the single-threshold charging strategy used in the current terminals, and the results show that the dual-threshold charging strategy can effectively improve the average utilization efficiency of the AGVs by 5.21% to 10.07%, and reduce the task completion time of the container transport by about 3.31% on average. Finally, the impact of the number of AGVs on the task completion time and AGV utilization is shown by AGV quantity analysis, and the results show that when the number of Quay Cranes (QCs) and AGVs is about 1:4.5. This finding suggests that having an appropriate number of AGVs relative to QCs is essential for efficient terminal operations. Too few AGVs may lead to long waiting times for QCs, while too many AGVs may result in congestion and inefficient use of resources. Therefore, the optimal ratio provides a guideline for terminal operators to balance the number of AGVs and QCs to maximize operational efficiency. Moreover, the implementation of the dual-threshold charging strategy, and the application of the GAHA algorithm have collectively contributed to enhancing the operational efficiency of AGVs in a U-shaped ACT. These improvements not only reduce task completion time but also increase AGV utilization, leading to more efficient and productive terminal operations.</p>

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AGV scheduling at U-shaped automated container terminals considering dual threshold charging strategy

  • 期刊论文

  • 发表日期：

    2025年7月17日

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  • Tianhao Xie,
  • Fang Yu,
  • Yongsheng Yang

摘要

In order to enhance the operational efficiency of Automated Guided Vehicles (AGVs) within a container terminal, particularly in a U-shaped automated container terminal (ACT), a comprehensive analysis of terminal transportation system and storage yard layout is conducted. A dual-threshold charging strategy is proposed, which considers both allowable working power threshold and allowable charging power threshold, and an AGV scheduling model with the goal of minimizing task completion time is established. Meanwhile, a Genetic-ALNS Hybrid Algorithm (GAHA) is used to optimize the AGV scheduling sequence, combining genetic algorithm with adaptive large neighborhood search algorithm. The experimental results show that the GAHA algorithm solves the AGV scheduling problem with a better convergence speed and finds a better optimal solution than the adaptive genetic algorithm combining the greedy strategy and the genetic algorithm. In addition, the dual-threshold charging strategy is compared with the single-threshold charging strategy used in the current terminals, and the results show that the dual-threshold charging strategy can effectively improve the average utilization efficiency of the AGVs by 5.21% to 10.07%, and reduce the task completion time of the container transport by about 3.31% on average. Finally, the impact of the number of AGVs on the task completion time and AGV utilization is shown by AGV quantity analysis, and the results show that when the number of Quay Cranes (QCs) and AGVs is about 1:4.5. This finding suggests that having an appropriate number of AGVs relative to QCs is essential for efficient terminal operations. Too few AGVs may lead to long waiting times for QCs, while too many AGVs may result in congestion and inefficient use of resources. Therefore, the optimal ratio provides a guideline for terminal operators to balance the number of AGVs and QCs to maximize operational efficiency. Moreover, the implementation of the dual-threshold charging strategy, and the application of the GAHA algorithm have collectively contributed to enhancing the operational efficiency of AGVs in a U-shaped ACT. These improvements not only reduce task completion time but also increase AGV utilization, leading to more efficient and productive terminal operations.