Abstract
An optimized tailored nonlinear fluctuation smoothing rule is proposed in this study to improve the performance of scheduling jobs in a semiconductor manufacturing factory. The tailored nonlinear fluctuation smoothing rule is modified from the well-known fluctuation smoothing rules with some innovative treatments. At first, the fuzzy c-means and fuzzy back propagation network approach is applied to estimate the remaining cycle time of every job. Then the nonlinear fluctuation smoothing rules are constructed and tailored to the semiconductor manufacturing factory to be scheduled. Finally, the back propagation network optimization approach is proposed to optimize the rule. To evaluate the effectiveness of the proposed methodology, production simulation is also applied in this study to generate some test data. According to experimental results, the proposed methodology outperformed nine existing approaches in reducing the cycle time averages and standard deviations.
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