A reinforcement learning approach to re-entrant manufacturing system scheduling

In this paper, we focus on the problem of optimally scheduling a closed reentrant system with one type of parts and two service centers, each of which consisting of one machine. An algorithm based on reinforcement learning is proposed. The results of the experiments indicate that reinforcement learning can outperform some familar heuristic methods and is closed to the WB policy