Research on flexible job-shop scheduling problem based on variation-reinforcement learning

The main focus of this paper is to solve the optimization problem of minimizing the maximum completion time in the flexible job-shop scheduling problem. In order to optimize this objective, random sampling is employed to extract a subset of states, and the mutation operator of the genetic algorithm is used to increase the diversity of sample chromosomes. Additionally, 5-tuple are defined as the state space, and a 4-tuple is designed as the action space. A suitable reward function is also developed. To solve the problem, four reinforcement learning algorithms (Double-Q-learning algorithm, Q-learning algorithm, SARS algorithm, and SARSA(λ) algorithm) are utilized. This approach effectively extracts states and avoids the curse of dimensionality problem that occurs when using reinforcement learning algorithms. Finally, experimental results using an international benchmark demonstrate the effectiveness of the proposed solution model.