A post-classifying fuzzy-neural and data-fusion rule for job scheduling in a wafer fab - A simulation study

Tin-Chih Chen*, Yi Chi Wang

*Corresponding author for this work

研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)

摘要

This paper proposed a post-classifying fuzzy-neural and data-fusion rule to improve the performance of job scheduling in a wafer fabrication factory (wafer fab). The proposed rule is a hybrid (fusion) of two well-known fluctuation smoothing rules - FSMCT and FSVCT. Several ways of data fusion [including normalised sum (NS), normalised product (NP), condensed normalised product (CNP), weighted normalised product (WNP), and dynamic weighted normalised product (DWNP)] were applied for this purpose. Besides, in order to enhance the scheduling performance of the rule, the post-classifying fuzzy back propagation network (FBPN) approach was applied to improve the forecasting accuracy of the remaining cycle time. To evaluate the effectiveness of the proposed methodology, a production simulation was carried out. According to the experimental results, the proposed methodology outperformed some existing approaches by simultaneously reducing the average cycle time and cycle time variation.

原文English
頁(從 - 到)150-170
頁數21
期刊International Journal of Manufacturing Research
8
發行號2
DOIs
出版狀態Published - 22 四月 2013

指紋 深入研究「A post-classifying fuzzy-neural and data-fusion rule for job scheduling in a wafer fab - A simulation study」主題。共同形成了獨特的指紋。

引用此