强化学习下浅充浅放充电策略AGV调度研究  

Research on AGV scheduling of shallow charging and shallowdischarging charging strategy under reinforcement learning

作　　者：赵锐 梁承姬 Zhao Rui;Liang Chengji(Institute of Logistics Science and Engineering,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海事大学物流科学与工程研究院,上海201306

出　　处：《计算机应用研究》2024年第10期3038-3043,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(72271125);上海市青年科技英才扬帆计划资助项目(21YF1416400);上海市青年科技启明星计划资助项目(21QB1404800)。

摘　　要：针对自动化集装箱码头自动导引车(AGV)调度中的充电问题,考虑浅充浅放充电策略构建了混合整数优化模型。该模型以最小化AGV最终完工时间为目标,在考虑AGV电池电量变化以及AGV不同状态耗电差异的约束下,利用AGV空闲时间和一个作业循环结束时间补电,减少AGV充电次数,进而减少总完成时间。模型采用Wolf-PHC强化学习进行求解,并分别与GAMS求解器、Q-learning算法及遗传算法(genetic algorithm,GA)求解结果进行比较,以验证模型的有效性和算法的优越性。算例分析表明在浅充浅放充电策略下AGV利用效率较高,且Wolf-PHC与GA的结合对模型求解效果更佳。For charging problem in AGV scheduling in automated container terminals,this paper constructed a mixed integer optimization model considering the shallow charging and shallow discharging charging strategy.The model aimed to minimize the final completion time of the AGV.Under the constraints of considering the change of AGV battery power and the difference in power consumption in different states of the AGV,the model used the AGV idle time and the end time of a work cycle to make up power,reducing the number of AGV charging times,and thus reducing the total completion time.The model was solved by Wolf-PHC reinforcement learning,and the results were compared with GAMS solver,Q-learning algorithm and genetic algorithm(GA)respectively to verify the effectiveness of the model and superiority of the algorithm.The example analysis shows that AGV utilization efficiency is higher under the shallow charging and shallow discharging charging strategy,and the combination of Wolf-PHC and GA is better for the model solution.

关 键 词：自动化集装箱码头 自动导引车 浅充浅放充电策略 强化学习 遗传算法 

分 类 号：U691.3[交通运输工程—港口、海岸及近海工程]