Abstract:

In order to improve the efficiency of automated container terminal, focused on integrated scheduling problem of Automated Stacking Crane（ASC） and Automated Guided Vehicle（AGV） at discharging process, a mixed-integer programming model is proposed to minimize the total operating time and total delay time, determine buffer allocation and optimize operational sequence of ASC by considering the time window and buffer capacity constraints. Comparative experiments show that the results of genetic algorithm are better than that of branch-and-bound in large-scale cases, and the genetic algorithm can find the ideal solution in finite time to verify the validity of the model and algorithm. The result of sensitivity experiment shows that the increase of ASC operational time has a significant effect on the delay time of the total task, and the rapid increase of the delay time of the total task causes the rapid increase of the objective function value.

Key words: automated container terminal, Automated Guided Vehicle（AGV）, buffer, genetic algorithm, integrated scheduling

摘要：

为提高自动化集装箱码头作业效率，针对卸船过程中自动化堆垛起重机（Automated Stacking Crane，ASC）与自动化导引小车（Automated Guided Vehicle，AGV）的集成调度问题，考虑缓冲区容量约束，以最小化总任务完成时间和总任务延迟时间为目标，建立带时间窗的混合整数规划模型，确定任务与缓冲位的分配关系，优化ASC的作业顺序。对比实验分析表明，在大规模算例上，遗传算法的目标函数值逐渐优于分支定界法，且遗传算法能在有限时间求出理想解，验证模型和算法的有效性。灵敏度实验分析表明，ASC作业时间的增加对总任务延迟时间有显著影响，总任务延迟时间的快速增加引起目标函数值的快速增加。

关键词: 自动化集装箱码头, 自动化导引小车（AGV）, 缓冲区, 遗传算法, 集成调度