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

Authors: Toly Chen; Yi-Chi Wang

Addresses: Department of Industrial Engineering and Systems Management, Feng Chia University, 100, Wenhwa Rd., Seatwen, Taichung City 408, Taiwan ' Department of Industrial Engineering and Systems Management, Feng Chia University, 100, Wenhwa Rd., Seatwen, Taichung City 408, Taiwan

Abstract: 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. [Received 10 November 2011; Revised 17 February 2012; Accepted 11 May 2012]

Keywords: fluctuation smoothing; data fusion; post-classification; remaining cycle time; wafer fabrication; semiconductor manufacturing; fuzzy-neural rules; neural networks; fuzzy logic; job scheduling; simulation; forecasting accuracy; cycle time forecasting.

DOI: 10.1504/IJMR.2013.053285

International Journal of Manufacturing Research, 2013 Vol.8 No.2, pp.150 - 170

Published online: 29 Jan 2014 *

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