The application of actor-critic reinforcement learning for fab dispatching scheduling
- Resource Type
- Conference
- Authors
- Kim, Namyong; Shin, Hayong
- Source
- 2017 Winter Simulation Conference (WSC) Simulation Conference (WSC), 2017 Winter. :4570-4571 Dec, 2017
- Subject
- Computing and Processing
General Topics for Engineers
Economic indicators
- Language
- ISSN
- 1558-4305
This paper applies Actor-Critic reinforcement learning to control lot dispatching scheduling in reentrant line manufacture model. To minimize the Work-In-Process(WIP) and Cycle Time(CT), the lot dispatching policy is directly optimized through Actor-Critic algorithm. The results show that the optimized dispatching policy yields smaller average WIP and CT than traditional dispatching policy such as Shortest Processing Time, Latest-Step-First-Served, and Least-Work-Next-Queue.