Surrogate-Assisted Symbiotic Organisms Search Algorithm for Parallel Batch Processor Scheduling

Zheng Cai Cao, Cheng Ran Lin, Meng Chu Zhou, Jia Qi Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 languageEnglish (US)
Article number9099099
Pages (from-to)2155-2166
Number of pages12
JournalIEEE/ASME Transactions on Mechatronics
Volume25
Issue number5
DOIs
StatePublished - 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

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