A fuzzy-neural fluctuation smoothing rule for scheduling jobs with various priorities in a miconductor manufacturing factory

Tin-Chih Chen*, Yu Cheng Lin

*Corresponding author for this work

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

A fuzzy-neural fluctuation smoothing rule is proposed in this study to improve the performance of scheduling jobs with various priorities in a semiconductor manufacturing factory. The fuzzy-neural fluctuation smoothing rule is modified from the well-known fluctuation smoothing rule by improving the accuracy of estimating the remaining cycle time of a job, which is done by applying Chen's fuzzy-neural approach with multiple buckets. To evaluate the effectiveness of the proposed methodology, production simulation is also applied in this study. According to experimental results, incorporating a more accurate remaining cycle time estimation mechanism did improve the scheduling performance especially in reducing the average cycle times. Besides, the fuzzy-neural fluctuation smoothing rule was also shown to be a Pareto optimal solution for scheduling jobs with various priorities in a semiconductor manufacturing factory.

Original languageEnglish
Pages (from-to)397-417
Number of pages21
JournalInternational Journal of Uncertainty, Fuzziness and Knowlege-Based Systems
Volume17
Issue number3
DOIs
StatePublished - 1 Jun 2009

Keywords

  • Femaining cycle time
  • Fluctuation smoothing
  • Fuzzy-neural
  • Scheduling
  • Semiconductor manufacturing
  • Simulation

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