Abstract
Parallel batch processor scheduling with dynamic job arrival is complex and challenging in semiconductor manufacturing. In order to get its reliable and high-performance schedule in a reasonable time, this work decomposes this scheduling problem into two-stage solution strategy: a batch forming subproblem and a batch scheduling subproblem. The batch formation is made by a heuristic rule. Then, a surrogate-assisted symbiotic organisms search algorithm with a new encoding mechanism is utilized to search for the optimal batch schedule, which integrates a surrogate model and a parameter control scheme. The surrogate model, which can predict the sequencing result instead of time-consuming true fitness evaluation, is used to reduce the computational burden greatly. In this article, a parameter control scheme based on reinforcement learning is proposed to balance the global and local search of symbiotic organisms search algorithm, as a guide for searching an assignment scheme. Finally, the experimental results demonstrate that the proposed algorithm can significantly improve the quality of a solution and save computational time via parameter control scheme and surrogate model.
Original language | English (US) |
---|---|
Article number | 9099099 |
Pages (from-to) | 2155-2166 |
Number of pages | 12 |
Journal | IEEE/ASME Transactions on Mechatronics |
Volume | 25 |
Issue number | 5 |
DOIs | |
State | Published - Oct 2020 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering
Keywords
- Parallel batch processor scheduling
- reinforcement learning (RL)
- surrogate model
- symbolic organisms search algorithm