Abstract: In order to improve operation efficiency of AGV in automated container terminals, this paper has established a scheduling model that gives consideration to the automated terminal AGV job in the process of charging with genetic algorithm under constraint conditions of post-charging cruising capacity of AGV. Scheduling model is established upon charging need of electricity-driven AGV job and characteristics of transportation process while taking impact of vertical layout of container yards and AGV charging process on actual operation. It aims at maximizing AGV charging utility rate and minimizing the task completion time as well as AGV no-load time. Through analysis of examples, this paper has compared the solution effects of genetic algorithm and mixed integer programming algorithm, analyzed the impact of AGV quantity that is involved in transportation on transportation time and verified the credibility of the scheduling plan attained with genetic algorithm. Finally, a conclusion is drawn：as with this problem, genetic algorithm can work out a reliable AGV scheduling plan quickly and effectively.

Key words: automated container terminal, AGV scheduling policy, mixed-integer programming, genetic algorithm, electric vehicle

摘要： 为了提高自动化集装箱码头AGV（Automated Guided Vehicle）的作业效率，根据采用电力驱动的AGV作业时的充电需求和运输过程的特性，考虑了垂岸式集装箱堆场布局和AGV充电过程对实际作业的影响，以最大化AGV充电利用率、最小化最末任务完成时间、最小化AGV空载时间为目标，以AGV充电后的续航能力等为约束条件，以遗传算法为研究方法，构建了考虑充电过程的自动化码头AGV作业的调度模型。通过算例分析，对比了遗传算法与混合整数规划算法的求解效果，分析了参与运输的AGV数量对运输时间的影响，也验证了遗传算法给出的调度方案的可信性。最后得出结论：针对该问题，遗传算法可以快速、高效地给出值得信赖的AGV调度方案。

关键词: 自动化集装箱码头, AGV调度, 混合整数规划, 遗传算法, 电动汽车