NDLI: A nonlinear scheduling rule incorporating fuzzy-neural remaining cycle time estimator for scheduling a semiconductor manufacturing factory

Content Provider	SpringerLink
Author	Chen, Toly Wang, Yi Chi
Copyright Year	2009
Abstract	A nonlinear scheduling rule incorporating a fuzzy-neural remaining cycle time estimator is proposed in this study to improve scheduling performance in a semiconductor manufacturing factory. The proposed scheduling rule is modified from the well-known fluctuation smoothing rule with three treatments. At first, the look-ahead self-organization map-fuzzy back-propagation network approach in our previous study is used to estimate the remaining cycle time of every job in the semiconductor manufacturing factory. Subsequently, the release time and remaining cycle time of a job are both normalized to balance their importance in the fluctuation smoothing rule. Finally, the normalized release time is divided by the normalized remaining cycle time to obtain the slack. In this way, the proposed scheduling rule becomes a nonlinear one. To evaluate the effectiveness of the proposed methodology, production simulation is used to generate some test data. According to experimental results, the proposed methodology outperformed many existing approaches in reducing both the average cycle times and cycle time standard deviations. The advantage was up to 41% over the basis p-FS policy when the cycle time standard deviations were to be minimized.
Starting Page	110
Ending Page	121
Page Count	12
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	45
Issue Number	1-2
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-02-12
Publisher Place	London
Access Restriction	Subscribed
Subject Keyword	Nonlinear Scheduling Fluctuation smoothing Fuzzy-neural Remaining cycle time Simulation Semiconductor manufacturing Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Industrial and Manufacturing Engineering Mechanical Engineering Computer Science Applications Software

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