 An evolving hybrid neural approach for predicting job completion time in a semiconductor fabrication plantToly Chen and Yi-Chi Wang European Journal of Industrial Engineering, 2010, vol. 4, issue 3, 336-354 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. [Received 10 October 2008; Revised 05 March 2009; Revised 13 June 2009; Accepted 14 June 2009] Keywords: job completion times; semiconductor fabrication; look-ahead; backpropagation networks; neural networks; BPN; self-organising maps; SOM; genetic algorithms; simulation; production planning. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:eujine:v:4:y:2010:i:3:p:336-354 Access Statistics for this article More articles in European Journal of Industrial Engineering from Inderscience Enterprises Ltd |
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