Solving flexible job shop scheduling problems via deep reinforcement learning

Abstract

Flexible job shop scheduling problem (FJSSP), as a variant of the job shop scheduling problem, has a larger solution space. Researchers are always looking for good methods to solve this problem. In recent years, the deep reinforcement learning (DRL) has been applied to solve various shop scheduling problems due to its advantages that fast solving speed and strong generalization ability. In this paper, we first propose a new DRL framework to realize representation learning and policy learning. The new framework adopts a lightweight multi-layer perceptron (MLP) as the state embedding network to extract state information, which reduces the computational complexity of the algorithm to some extent. Next, we design a new state representation and define a new action space. The new state representation can directly reflect the state features of candidate actions, which is conducive for the agent to capture more effective state information and improve its decision-making ability. The new definition of action space can solve the two subproblems of the FJSSP simultaneously with only one action space. Finally, we evaluate the performance of the policy model on four public datasets: Barnes dataset, Brandimarte dataset, Dauzere dataset and Hurink dataset. Extensive experimental results on these public datasets show that the proposed method achieves a better compromise in terms of optimization ability and applicability compared to the composite priority dispatching rules and the existing state-of-the-art models.