A reinforcement learning approach for developing routing policies in multi-agent production scheduling

Most recent research studies on agent-based production scheduling have focused on developing negotiation schema for agent cooperation. However, successful implementation of agent-based approaches not only relies on the cooperation among the agents, but the individual agent’s intelligence for making good decisions. Learning is one mechanism that could provide the ability for an agent to increase its intelligence while in operation. This paper presents a study examining the implementation of the Q-learning algorithm, one of the most widely used reinforcement learning approaches, for use by job agents when making routing decisions in a job shop environment. A factorial experiment design for studying the settings used to apply Q-learning to the job routing problem is carried out. This study not only investigates the effects of this Q-learning application but also provides recommendations for factor settings and useful guidelines for future applications of Q-learning to agent-based production scheduling.