NDLI: A fuzzy-neural approach for estimating the monthly output of a semiconductor manufacturing factory

Content Provider	Springer Nature Link
Author	Chen, Toly
Copyright Year	2007
Abstract	Estimating the monthly output of each product type in a semiconductor manufacturing factory is a critical task for the plant. The future revenues and/or profits of the factory can be precisely evaluated only after the monthly outputs over the future months are accurately estimated. There are two steps in estimating the monthly output: predicting the completion time for every job in the factory, and then projecting the outputs into each future month. For enhancing the effectiveness of estimating the monthly output, a fuzzy-neural approach is proposed in this study. Firstly, the look-ahead self-organization map fuzzy back-propagation network (SOM-FBPN) approach is applied to predict the completion time for every job in the factory. Then, the fuzzy monthly output function (FMOF) is proposed to project the outputs into each future month. The job completion time forecast generated by the look-ahead SOM-FBPN approach is an asymmetric triangular fuzzy number (TFN), and is skewed to the side that contains the actual value. Such a property was shown to theoretically contribute to the advantage of the proposed FMOF function over a crisp or probabilistic function. Experimental results also confirmed the effectiveness of the proposed methodology in predicting the monthly output and in estimating the monthly revenues and profits.
Starting Page	589
Ending Page	598
Page Count	10
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	39
Issue Number	5-6
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-09-22
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Monthly output Semiconductor Self-organization map Fuzzy back-propagation network Fuzzy monthly output function Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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