A Parallel Learning Approach for the Flexible Job Shop Scheduling Problem

Reinforcement learning is emerging to achieve real-time response and near-optimization for solving the flexible job shop scheduling problem (FJSP), an important and NP-hard problem for intelligent manufacturing systems. Although some methods based on reinforcement learning have been proposed to solve the FJSP, there's still room for improvement. In this paper, we propose a new approach called reinforcement learning with the generative adversarial network (RLGAN) based on the parallel learning framework. A simulation-based artificial workshop system is established to generate a large number of sample plans as a training set for RLGAN to develop a near-optimal scheduling model. The case study shows that an implementation of our proposed method, QTRAN-GAN, can generate near-optimal plans and outperforms the corresponding pure reinforcement learning method, QTRAN.