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, a more 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.
Highlights
This study attempts to optimize the performance of a job dispatching rule in a wafer fabrication factory
Many semiconductor manufacturing factories suffer from lengthy cycle times and are not able to improve on their delivery promises to their customers
We investigated the job dispatching for this stage
Summary
This study attempts to optimize the performance of a job dispatching rule in a wafer fabrication factory. This was followed by Chen [14], in which he proposed the onefactor-tailored NFSMCT (1f-TNFSMCT) rule and the onefactor-tailored nonlinear FSVCT (1f-TNFSVCT) rule. Both rules contain an adjustable parameter to allow them to be customized for a target wafer fabrication factory. In a multiple-objective study, Chen and Wang [16] proposed a biobjective nonlinear fluctuation smoothing rule with an adjustable factor (1f-biNFS) to optimize both the average cycle time and the cycle time variation at the same time. According to Chen and Wang [3], with more accurate remaining cycle time estimation, the scheduling performance of a fluctuation smoothing rule can be significantly improved. Yes (i) Fusing FSVCT and FSMCT (ii) Adding adjustable parameters (i) Fusing 2f-TFSMCT and
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