An evolving hybrid neural approach for predicting job completion time in a semiconductor fabrication plant

Tin-Chih Chen*, Yi Chi Wang

*Corresponding author for this work

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Predicting job completion time is an important but difficult task to a semiconductor fabrication plant. To further enhance its effectiveness, an evolving hybrid neural approach is proposed in this study. To evaluate the effectiveness of the proposed approach, Production Simulation (PS) is also employed to generate test data. According to experimental results, the predicting accuracy of the evolving hybrid neural approach is significantly better than those of many existing approaches. In addition, to improve the practicability of the evolving hybrid neural approach, several issues in practical applications are addressed and discussed. Though the proposed evolving hybrid neural approach seems to be theoretically complicated, its ease of implementation on the production planning and control for a semiconductor plant is demonstrated in this study.

Original languageEnglish
Pages (from-to)336-354
Number of pages19
JournalEuropean Journal of Industrial Engineering
Volume4
Issue number3
DOIs
StatePublished - 1 Jun 2010

Keywords

  • BPN
  • Back propagation network
  • Completion time
  • Genetic algorithm
  • Look-ahead
  • SOM
  • Self-organising map
  • Semiconductor fabrication

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