An Improved QMIX-Based AGV Scheduling Approach for Material Handling Towards Intelligent Manufacturing

With the advent of Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) technology provide enormous opportunities and support for intelligent manufacturing. Material handling is vital in manufacturing systems to ensure that proper materials with the right quantity and quality can be delivered to each machine or workstation at the right time. AGV has been widely used in smart factories for material handling, and AGV scheduling plays a critical role in practical AGV application. However, the AGV scheduling problem becomes more and more complex with the development of intelligent manufacturing, making the design of effective and efficient scheduling algorithms complicated. In this paper, we analyzed the AGV dispatching tasks in the workshop and model the workshop as a node network, and apply an improved Multi-Agent Reinforcement Learning (MARL), that is, an improved QMIX model to solve the AGV scheduling problem. The experiment results show that the proposed approach outperforms the other commonly-used methods such as deep reinforcement learning (DQN) under different environment settings, in term of the maximum makespan of AGV.