AGV scheduling for order-driven intelligent workshop based on reinforcement learning

Material transporting efficiency has an important impact on the production scheduling efficiency of the intelligent workshop. Material transporting tasks are usually executed by automated guided vehicle (AGV), which have large number of tasks, real-time changes in task demand, and intensive task issuance. In order to make the AGV workflow timely, efficient and accurate, an reinforcement-learning-based AGVs' scheduling model was established with a two-level mechanism. The first level aimes for load balancing, and assigns the tasks to AGVs in a rule-based scheduling method. The second level plans each AGV's path by a reinforcement learning deep Q-network (DQN) algorithm with single agent, which can reduce the convergence difficulty of the scheduling algorithm by reducing the dimensions of the agent's action space. The effectiveness and innovation of the method was verified through simulation examples.