A nonlinear programming and artificial neural network approach for optimizing the performance of a job dispatching rule in a wafer fabrication factory

Authors:
Toly Chen
Affiliations:
Department of Industrial Engineering and Systems Management, Feng Chia University, Seatwen, Taichung, Taiwan
Venue:
Applied Computational Intelligence and Soft Computing - Special issue on Applied Neural Intelligence to Modeling, Control, and Management of Human Systems and Environments
Year:
2012

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Abstract

A nonlinear programming and artificial neural network approach is presented in this study to optimize the performance of a job dispatching rule in a wafer fabrication factory. The proposed methodology fuses two existing rules and constructs a nonlinear programming model to choose the best values of parameters in the two rules by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several studies. In addition, amore effective approach is also applied to estimate the remaining cycle time of a job, which is empirically shown to be conducive to the scheduling performance. The efficacy of the proposed methodology was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future.