Dynamic job-shop scheduling using reinforcement learning agents

Static and dynamic scheduling methods have attracted a lot of attention in recent years. Among these, dynamic scheduling techniques handle scheduling problems where the scheduler does not possess detailed information about the jobs, which may arrive at the shop at any time. In this paper, an intelligent agent based dynamic scheduling system is proposed. It consists of two independent components: the agent and the simulated environment. The agent selects the most appropriate priority rule according to the shop conditions in real time, while simulated environment performs scheduling activities using the rule selected by the agent. The agent is trained by an improved reinforcement learning algorithm through the learning stage and then it successively makes decisions to schedule the operations. © 2000 Elsevier Science B.V. All rights reserved.