Job cycle time estimation in a wafer fabrication factory with a bi-directional classifying fuzzy-neural approach

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Estimating the cycle time for every job in a factory is a critical task. It was recently reported that job classification noticeably enhanced the accuracy of job cycle time estimation. In pre-classifying approaches, whether the pre-classification approach combined with the subsequent estimation approach is suitable for the data is questionable. Conversely, the difficulty in classifying a job according to only the estimation error not the various attributes is a problem to post-classifying approaches. To tackle these problems, a bi-directional classifying fuzzy-neural approach is proposed in this study. In the proposed methodology, jobs are not only pre-classified but also post-classified. The results of pre-classification and post-classification are aggregated into a suitability index for each job. A job is then assigned to the category to which its suitability index is the highest. A radial basis function network is also constructed to predict the suitability index of a job according to the various attributes. To evaluate the effectiveness of the proposed methodology, a practical example was used in this study. According to experimental results, the estimation accuracy of the proposed methodology was significantly better than those of many existing approaches.

Original languageEnglish
Pages (from-to)1007-1018
Number of pages12
JournalInternational Journal of Advanced Manufacturing Technology
Volume56
Issue number9-12
DOIs
StatePublished - 1 Oct 2011

Keywords

  • Cycle time
  • Estimation
  • Fuzzy back propagation network
  • Fuzzy c-means
  • Radial basis function network
  • Wafer

Fingerprint Dive into the research topics of 'Job cycle time estimation in a wafer fabrication factory with a bi-directional classifying fuzzy-neural approach'. Together they form a unique fingerprint.

  • Cite this