With the continuous increase in labour costs and the demands of the supply chain, improving the efficiency of automated container terminals has been a key factor in the development of ports. Automated guided vehicles (AGVs) are the main means of horizontal transport in such terminals, and problems in relation to their use such as vehicle conflict, congestion and waiting times have become very serious, greatly reducing the operating efficiency of the terminals. In this article, we model the minimum driving distance of AGVs that transport containers between quay cranes (QCs) and yard cranes (YCs). AGVs are able to choose the optimal path from pre-planned paths by testing the overlap rate and the conflict time. To achieve conflict-free AGV path planning, a priority-based speed control strategy is used in conjunction with the Dijkstra depth-first search algorithm to solve the model. The simulation experiments show that this model can effectively reduce the probability of AGVs coming into conflict, reduce the time QCs and YCs have to wait for their next task and improve the operational efficiency of AGV horizontal transportation in automated container terminals.

中文翻译：

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自动化集装箱码头自动引导车辆路径规划的基于优先级的速度控制策略

随着劳动力成本的不断增加和供应链的需求，提高自动化集装箱码头的效率已经成为港口发展的关键因素。自动导引车（AGV）是此类码头的主要横向运输工具，其使用中存在的车辆冲突、拥堵、等待时间等问题非常严重，大大降低了码头的运营效率。在本文中，我们对在码头起重机 (QC) 和堆场起重机 (YC) 之间运输集装箱的 AGV 的最小行驶距离进行建模。AGV 能够通过测试重叠率和冲突时间从预先规划的路径中选择最佳路径。实现无冲突AGV路径规划，基于优先级的速度控制策略与 Dijkstra 深度优先搜索算法结合使用来求解模型。仿真实验表明，该模型可以有效降低AGV发生冲突的概率，减少QC和YC等待下一个任务的时间，提高AGV水平运输在自动化集装箱码头的运行效率。