Simulation and optimization of AGV operations scheduling at automated container terminals based on deep-reinforcement learning
To address the challenge of visualizing real-time scheduling in the traditional mathemati-cal model of Automatic Guided Vehicle(AGV)scheduling within automated container terminals and,the difficulty in enhancing scheduling strategy efficiency in simulation models,this study ex-plores the interaction path method between deep reinforcement learning algorithms and the AnyLog-ic automated container terminal simulation model,leveraging established simulation and operation planning models.Subsequently,this study leverages the AGV operations data of an automated con-tainer terminal import box simulation model to train the network model of the deep-reinforcement learning algorithm under the condition of low task generation rate,and loads it into the simulation model of the high task and low task generation rate to realize efficient scheduling of AGV in the model.This approach effectively overcomes the bottleneck of improving strategy efficiency within the AnyLogic system and the limitations associated with using the CPLEX tool to solve operation planning mathematical models outside the system,which encounter difficulties in handling large-scale data and complex solving processes.Experimental results demonstrate that compared to strate-gies with the best performance defined in the AnyLogic system and those solved by CPLEX out-side the system,the efficiency of deep-reinforcement learning DDQN algorithms in AGV opera-tions scheduling in front of storage yard of simulation models with low task generation rates is im-proved by an average of 522 and 1604 s,respectively.In the high task generation rate of the auto-mated terminal simulation model,AGV operations scheduling in both the front and back yards is improved by an average of 3000 s compared to custom strategies within the system.The interac-tion path method between deep-reinforcement learning algorithms and the AnyLogic simulation model not only enables visual real-time dynamic scheduling but also enhances the efficiency of AGV operations scheduling and the overall effectiveness of the automated container terminal sim ulation model.
万方数据
维普
