基于改进DQN算法的自动化码头AGV调度问题研究

AGV Scheduling Problem at Automated Terminals Based on Improved DQN Algorithm

针对自动化码头AGV(automated guided vehicle)调度问题,提出了一种考虑未来任务的深度Q网络(future tasks considering deep Q-network,F-DQN)算法指导AGV进行实时调度。对系统状态进行了改进,结合实时调度和静态调度的优点,在做出实时决策时考虑了静态的未来任务信息,以获得更优的调度方案。以洋山四期自动化码头的真实布局和设备情况为参考,使用仿真软件Plant Simulation进行了一系列仿真实验。实验结果表明:F-DQN算法可以有效解决自动化码头AGV实时调度问题,且F-DQN算法相比于传统DQN算法,能够显著缩短岸桥的等待时间。

A future tasks considering deep Q-network(F-DQN)algorithm was proposed to output realtime scheduling results of automated guided vehicles(AGVs)at automated terminals.This algorithm combined the advantages of real-time scheduling and static scheduling,improving the system status by considering static future task information when making real-time decisions,so as to obtain a better scheduling solution.In this study,the actual layout and equipment conditions of the Yangshan phase IV automated terminal were considered,and a series of simulation experiments were conducted using the Plant Simulation software.The experimental results show that the F-DQN algorithm can effectively solve the real-time scheduling problem of AGVs at automated terminals.Furthermore,the F-DQN algorithm significantly reduces the waiting time of quay cranes compared to the traditional DQN algorithm.