计算机应用研究2024，Vol.41Issue(10)：3038-3043,6.DOI:10.19734/j.issn.1001-3695.2024.03.0057
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 shallow discharging charging strategy under reinforcement learning
摘要
Abstract
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 algo-rithm(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Key words
automated container terminal/automatic guided vehicle(AGV)/shallow charge and shallow discharge charging strategy/reinforcement learning/genetic algorithm分类
交通工程引用本文复制引用
赵锐,梁承姬..强化学习下浅充浅放充电策略AGV调度研究[J].计算机应用研究,2024,41(10):3038-3043,6.基金项目
国家自然科学基金资助项目(72271125) （72271125）
上海市青年科技英才扬帆计划资助项目(21YF1416400) （21YF1416400）
上海市青年科技启明星计划资助项目(21QB1404800) （21QB1404800）
