Abstract
A dynamic and real-time system integrating reinforcement learning with simulation is designed for job-shop scheduling. Several stochastic variables are introduced to transform the job-shop scheduling problem into sequential decision problem. The model environment of job-shop scheduling is built by simulation for obtaining the system performance indices and ensuring the feasibility of the solution. Then, a multi-agent Q-learning algorithm integrated with simulation is developed to solve the job-shop problem. Finally, simulation and optimization experiments show the effectiveness of the system.
| Original language | English |
|---|---|
| Pages (from-to) | 675-679 |
| Number of pages | 5 |
| Journal | Kongzhi yu Juece/Control and Decision |
| Volume | 22 |
| Issue number | 6 |
| State | Published - Jun 2007 |
Keywords
- Job-shop scheduling
- Reinforcement learning
- Sequential decision
- Simulation
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