Dynamic flexible job shop scheduling algorithm based on deep reinforcement learning
- Resource Type
- Conference
- Authors
- Zhao, Tianrui; Wang, Yanhong; Tan, Yuanyuan; Zhang, Jun
- Source
- 2023 35th Chinese Control and Decision Conference (CCDC) Control and Decision Conference (CCDC), 2023 35th Chinese. :5099-5104 May, 2023
- Subject
- General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Schedules
Job shop scheduling
Scheduling algorithms
Heuristic algorithms
Simulation
Reinforcement learning
Dynamic scheduling
proximal policy optimization algorithm
deep reinforcement learning
dispatching rules
flexible job shop scheduling
- Language
- ISSN
- 1948-9447
The dynamic scheduling problem is a hot topic of current research. To solve the dynamic flexible job shop scheduling problem, an improved composite scheduling rule algorithm based on proximal policy optimization is proposed with the objective of minimizing the total delay time. The algorithm uses seven state features to represent the scheduling environment and designs six custom composite scheduling rules and a reward function. The algorithm is able to continuously interact with the environment to accumulate data and update the neural network parameters using Adam’s algorithm through offline learning. Simulation results show that the algorithm can achieve better performance metrics by using a combination of single scheduling rules, and the results are better compared to classical scheduling algorithms.