A data-mining-based localized scheduling system for a wafer fab

Horng Ren Tsai*, Tin-Chih Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This paper constructs a data-mining-based localized scheduling system to improve the performance of scheduling jobs in a wafer fabrication factory (wafer fab). The system is modified from Chen's tailored nonlinear fluctuation smoothing (TNFS) rule with some innovative treatments. First, the remaining cycle time of a job to be scheduled is estimated with a data mining approach instead to improve the accuracy. Second, in the original TNFS rule, the adjustable factor is static, while in this system it becomes dynamic. Third, the adjustable factor is also dependent on the stages of the jobs to be scheduled. Namely, the TNFS rule is localized. To evaluate the effectiveness of the proposed methodology, production simulation was also applied in this study. According to experimental results, the proposed methodology outperformed some existing approaches in reducing the average cycle time and cycle time variation.

Original languageEnglish
Pages (from-to)89-96
Number of pages8
JournalInternational Review on Computers and Software
Issue number1
StatePublished - 1 Jan 2010


  • Data mining
  • Dynami
  • Localized
  • Scheduling
  • Wafer fab

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