A novel method for solving dynamic flexible job-shop scheduling problem via DIFFormer and deep reinforcement learning

Abstract

Due to the dynamic changes of manufacturing environments, heuristic scheduling rules are unstable in dynamic scheduling. Although meta-heuristic methods provide the best scheduling quality, their solution efficiency is limited by the scale of the problem. Therefore, a novel method for solving the dynamic flexible job-shop scheduling problem (DFJSP) via diffusion-based transformer (DIFFormer) and deep reinforcement learning (D-DRL) is proposed. Firstly, the DFJSP is modeled as a Markov decision process, where the state space is constructed in the form of the heterogeneous graph and the reward function is designed to minimize the makespan and maximize the machine utilization rate. Secondly, DIFFormer is used to encode the operation and machine nodes to better capture the complex dependencies between nodes, which can effectively improve the representation ability of the model. Thirdly, a selective rescheduling strategy is designed for dynamic events to enhance the solution quality of DFJSP. Fourthly, the twin delayed deep deterministic policy gradient (TD3) algorithm is adopted for training an efficient scheduling model. Finally, the effectiveness of the proposed D-DRL is validated through a series of experiments. The results indicate that D-DRL achieves better solution quality and higher solution efficiency when solving DFJSP instances.