考虑冲突和拥堵的自动导引车调度与路径规划协同优化

针对自动化集装箱码头自动导引车（AGV）调度与无冲突路径规划问题，提出了AGV冲突拥堵解决策略以生成无冲突路径。首先，考虑堆场缓冲支架的容量，运行路径无拥堵、节点无冲突约束，以最大完工时间最小、AGV总行驶时间最短为目标建立两阶段混合整数规划模型；其次，设计改进的自适应遗传算法、基于冲突拥堵解决策略的迪杰斯特拉算法求得AGV调度方案与无冲突路径。算例分析结果表明：改进的自适应遗传算法相较遗传算法平均求解时间降低了13.56%，且目标函数平均差距率为9.01%；基于冲突拥堵解决策略相较停车等待策略使得水平运输区拥堵度降低67.6%，AGV等待时间减少66.7%。可见，所提算法求解质量高且速度快，同时验证了所提策略的有效性。

Collaborative optimization of automated guided vehicle scheduling and path planning considering conflict and congestion

In order to solve the problems of Automated Guided Vehicle （AGV） scheduling and conflict-free path planning in automated container terminals， an AGV conflict and congestion resolution strategy was proposed to generate conflict-free paths. Firstly， considering the capacity of the buffer bracket in the container yard as well as the constraints of no congestion on the operation paths and no conflict on the nodes， a two-stage mixed integer programming model was established based on the goal of the smallest maximum completion time and the shortest AGV transportation time. Then， an improved adaptive genetic algorithm and Dijkstra algorithm based on conflict and congestion resolution strategy were designed to obtain the AGV scheduling scheme and conflict-free paths. The results of numerical examples show that the improved adaptive genetic algorithm has the average solution time reduced by 13.56%， and the average gap rate of the objective function reduced by 9.01% compared to the genetic algorithm. Compared with the parking to wait strategy， the conflict and congestion resolution strategy has the congestion rate of the horizontal transportation area reduced by 67.6%， and the AGV waiting time reduced by 66.7%. It can be seen that the proposed algorithm has higher solving quality and faster speed， at the same time， the effectiveness of the proposed strategy is verified.