Abstract: In order to study the coordinated scheduling of Automated Guided Vehicle（AGV） and Double-Trolley Quay Crane（QC） in automated container terminals, considering the double-trolley pontoon bridge transfer platform and its capacity constraints, and with the constraints of the Portal Trolley（PT） time window, a mixed-integer programming model is established with the goal of minimizing the maximum completion time of container tasks. A heuristic algorithm is designed to obtain the time window of the quayside gantry crane operation container task from the capacity of the transfer platform. The genetic algorithm is used to solve the AGV scheduling optimization program and the coordination scheduling problem of the two major equipments. Finally, 10 groups of experiments are used as examples to compare the optimization results of genetic algorithm and particle swarm optimization. The results show that the two algorithms are consistent, and the convergence speed of the model based on genetic algorithm is faster, thus verifying the feasibility of this method.

Key words: automated container terminal, double-trolley quay crane, transfer platform, time window, AGV scheduling, genetic algorithm

摘要： 为研究自动化集装箱码头中自动导引运输车（Automated Guided Vehicle，AGV）与双小车岸桥（Double-Trolley Quay Crane，QC）的协调调度问题，考虑双小车岸桥中转平台及其容量限制，并以双小车岸桥门架小车时间窗为约束，建立以集装箱任务最大完工时间最小化为目标的混合整数规划模型。设计启发式算法，由中转平台的容量求得岸桥门架小车操作集装箱任务的时间窗，并采用遗传算法进行求解，给出相应的AGV调度优化方案，解决两大设备的协调调度问题。最后，以10组实验为例，比较了遗传算法与粒子群算法的优化结果。结果表明两种算法一致，且基于遗传算法的模型求解收敛速度更快，从而验证了该算法的可行性。

关键词: 自动化集装箱码头, 双小车岸桥, 中转平台, 时间窗, AGV调度, 遗传算法