Title: Hybridising PCA and BPN for job flow time forecasting in a wafer fabrication factory
Author: Toly Chen
Address: Department of Industrial Engineering and Systems Management, Feng Chia University, 100, Wenhwa Rd., Seatwen, Taichung City 408, Taiwan
Abstract: Principal Component Analysis (PCA) is a multivariate statistical analysis method. This method constructs a series of linear combinations of the original variables to form a new variable, so that these new variables are unrelated to each other as much as possible, to reflect information in a better way. A PCA and Back Propagation Network (PCA-BPN) approach is proposed in this study for forecasting the flow time of a job in a wafer fabrication factory, which is a critical task to the wafer fabrication factory. For evaluating the effectiveness of the proposed methodology, Production Simulation (PS) is also applied in this study to generate some test data.
Keywords: wafer fabrication; PCA; principal component analysis; BPN; back propagation networks; flow time forecasting; neural networks; simulation.
Int. J. of Technology Intelligence and Planning, 2011 Vol.7, No.4, pp.281 - 290
Available online: 22 Jan 2012