Hybridising PCA and BPN for job flow time forecasting in a wafer fabrication factory

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Principal Component Analysis (PCA) is a multivariate statistical analysis method. This method constructs a series of linear combinations of the original variables to form a new variable, so that these new variables are unrelated to each other as much as possible, to reflect information in a better way. A PCA and Back Propagation Network (PCA-BPN) approach is proposed in this study for forecasting the flow time of a job in a wafer fabrication factory, which is a critical task to the wafer fabrication factory. For evaluating the effectiveness of the proposed methodology, Production Simulation (PS) is also applied in this study to generate some test data.

Original languageEnglish
Pages (from-to)281-290
Number of pages10
JournalInternational Journal of Technology Intelligence and Planning
Volume7
Issue number4
DOIs
StatePublished - 1 Jan 2011

Keywords

  • BPN
  • Back propagation network
  • Flow time
  • PCA
  • Principal component analysis
  • Wafer fabrication factory

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