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Surrogate-Assisted Symbiotic Organisms Search Algorithm for Parallel Batch Processor Scheduling
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DOI：10.1109/TMECH.2020.2996911.png)
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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.
Keyword:
Processor scheduling
Batch production systems
Heuristic algorithms
Sequential analysis
Dynamic scheduling
Computational modeling
Parallel batch processor scheduling
reinforcement learning (RL)
surrogate model
symbolic organisms search algorithm
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