A fuzzy-neural knowledge-based system for job completion time prediction and internal due date assignment in a wafer fabrication plant

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

To further enhance the performance of job completion time prediction and internal due date assignment in a wafer fab, a fuzzy-neural knowledge-based system is constructed in this study. In the constructed system, multiple experts construct their own fuzzy multiple linear regression models from various viewpoints to predict the completion/cycle time of a job. Each fuzzy multiple linear regression model can be converted into an equivalent non-linear programming problem to be solved. Subsequently, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to aggregate the fuzzy completion time forecasts into a polygon-shaped fuzzy number, in order to improve the precision of completion time forecasting. The polygon-shaped fuzzy number contains the actual value, and its upper bound determines the internal due date of the job. After that, a back propagation network is constructed to defuzzify the polygon-shaped fuzzy number and to generate a representative/crisp value, so as to enhance the accuracy. A practical example is used to evaluate the effectiveness of the proposed methodology. According to experimental results, the proposed methodology improved both the precision and accuracy of job cycle time prediction by 16 and 21%, respectively.

Original languageEnglish
Pages (from-to)889-902
Number of pages14
JournalInternational Journal of Systems Science
Volume40
Issue number8
DOIs
StatePublished - 1 Aug 2009

Keywords

  • Fuzzy
  • Internal due date
  • Neural
  • Wafer fab

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