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

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