Mass customization with reinforcement learning: Automatic reconfiguration of a production line

Abstract

This paper addresses the problem of efficient automation system configuration for mass customization in industrial manufacturing. Due to the various demands from customers, production lines need to adjust the process parameters of the machines based on specific quality parameters. Reinforcement learning, which learns from samples, can tackle the problem more efficiently than the currently used methods. Based on the proximal policy optimization and centralized training with decentralized execution, a multi-agent reinforcement learning method (MARL) is proposed to reconfigure process parameters of machines based on the changed specifications. The proposed method has the actor of each agent observing only its own state, the agents are made to collaborate by a centralized critic which observes all the states. To evaluate the method, a steel strip rolling line with six collaborating mills is studied. Simulation results show that the proposed method outperforms the existing methods and state-of-the-art multi-agent reinforcement learning methods in terms of accuracy and computing costs.