Design patterns of deep reinforcement learning models for job shop scheduling problems

Abstract

Production scheduling has a significant role when optimizing production objectives such as production efficiency, resource utilization, cost control, energy-saving, and emission reduction. Currently, deep reinforcement learning-based production scheduling methods achieve roughly equivalent precision as the widely used meta-heuristic algorithms while exhibiting higher efficiency, along with powerful generalization abilities. Therefore, this new paradigm has drawn much attention and plenty of research results have been reported. By reviewing available deep reinforcement learning models for the job shop scheduling problems, the typical design patterns and pattern combinations of the common components, i.e., agent, environment, state, action, and reward, were identified. Around this essential contribution, the architecture and procedure of training deep reinforcement learning scheduling models and applying resultant scheduling solvers were generalized. Furthermore, the key evaluation indicators were summarized and the promising research areas were outlined. This work surveys several deep reinforcement learning models for a range of production scheduling problems.