随着集装箱运输需求的日益增长以及新型信息技术的广泛应用，集装箱码头作业自动化成为国内外港口发展的主要趋势。集装箱码头作业自动化可有效提高码头的作业效率和安全性，降低码头对人力资源的需求和运营成本。水平运输系统是集装箱码头装卸系统的重要组成部分，同时也是实现集装箱在码头前沿与堆场之间运转的重要纽带，其运行的稳定性和调度的合理性将直接影响码头自动化装卸系统的运行效率。自动导引车（AGV）是自动化集装箱码头常用的水平运输设备，承担着从前沿岸桥到后方堆场的集装箱运输任务。在实际作业过程中，多辆AGV同时运作难免会发生冲突和拥堵。基于此，文中采用基于冲突搜索的双层算法（CBS算法）解决码头多AGV协同作业产生的冲突问题，上层算法搜索AGV间的冲突，下层算法采用A*算法对AGV进行路径规划，并在A*算法中引入负载因子，使得规划路径避开拥堵点，实现码头道路的负载均衡。对于码头多任务点连续作业场景下的多车路径规划，基于CBS算法采用一种滑动时间窗冲突解决方法（STWCR）以提升运算效率。通过仿真实验验证了文中所提算法能够有效解决码头多AGV路径规划的冲突问题，同时均衡路网负载，缓解道路局部拥堵现象，提高道路资源的利用率。研究成果为自动化集装箱码头水平运输系统优化提供了参考。

With the increasing demand for container transportation and the widespread application of new information technologies, the automation of container terminal operations has become the main development trend in domestic and international ports. It can not only effectively improve the efficiency and safety of terminal operations, but also significantly reduce the demand for human resources and the operational costs. The horizontal transportation system is an essential part of the container terminal handling system and an important link enabling the highly efficient container transportation between the quayside and the storage yard, so its operational reliability and the reasonableness of the scheduling directly affect the operational efficiency of the automated container handling system. The mostly used horizontal transportation equipment in container terminals is the automated guided vehicles (AGVs), which is responsible for horizontal transportation from the front quay crane to the rear yard in automated container terminals. In actual operation process, conflicts and congestion is inevitable when multiple AGVs operate simultaneously. On this basis, this paper used conflict-based search (CBS) to solve the conflict problem arising from the cooperative operation of multi-AGVs at the terminal. The upper layer algorithm searched for conflicts among AGVs, while the lower layer algorithm used the A* algorithm for path planning of AGVs. A load factor was introduced into the heuristic function of the A* algorithm in order to avoid congestion in the path planning and achieve load balancing on terminal roads. Further, a sliding time window conflict resolution (STWCR) based on CBS was adopted to improve computational efficiency for multiple AGVs path planning in the continuous operation scenario of multiple task points at the terminal. Simulation experiments verified that the proposed algorithm in this paper can effectively solve the conflict problem of multiple AGVs path planning at the terminal, while balancing the road network load, alleviating local road congestion, and improving the utilization of road resources. The research results of this paper provide a reference for the optimization of the horizontal transportation system in automated container terminals.