A post-classifying fuzzy-neural and data-fusion rule for job scheduling in a wafer fab - A simulation study

Tin-Chih Chen*, Yi Chi Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This paper proposed a post-classifying fuzzy-neural and data-fusion rule to improve the performance of job scheduling in a wafer fabrication factory (wafer fab). The proposed rule is a hybrid (fusion) of two well-known fluctuation smoothing rules - FSMCT and FSVCT. Several ways of data fusion [including normalised sum (NS), normalised product (NP), condensed normalised product (CNP), weighted normalised product (WNP), and dynamic weighted normalised product (DWNP)] were applied for this purpose. Besides, in order to enhance the scheduling performance of the rule, the post-classifying fuzzy back propagation network (FBPN) approach was applied to improve the forecasting accuracy of the remaining cycle time. To evaluate the effectiveness of the proposed methodology, a production simulation was carried out. According to the experimental results, the proposed methodology outperformed some existing approaches by simultaneously reducing the average cycle time and cycle time variation.

Original languageEnglish
Pages (from-to)150-170
Number of pages21
JournalInternational Journal of Manufacturing Research
Volume8
Issue number2
DOIs
StatePublished - 22 Apr 2013

Keywords

  • Data fusion
  • Fluctuation smoothing
  • Post-classifying
  • Remaining cycle time
  • Wafer fab

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