A nonlinear fluctuation smoothing policy for production scheduling in a wafer fabrication factory

• Main Authors: Kai-Hsiang Yang, 楊凱翔
• Other Authors: Tin-Chih Chen
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/11717031463197442268

碩士 === 逢甲大學 === 工業工程與系統管理學研究所 === 97 === To further improve the performance of scheduling jobs in a wafer fabrication factory, a fuzzy-neural system is constructed in this study. The fuzzy-neural system is modified from the well-known fluctuation smoothing rule with three innovative treatments. At first, the remaining cycle time of a job is estimated by applying Chen et al’s fuzzy-neural approach to improve the estimation accuracy. Secondly, the components of the fluctuation smoothing rule are normalized, and then the division operator is applied instead of the traditional subtraction operator to enhance the responsiveness of the rule. Thirdly, the content of the fuzzy-neural system can be tailored for the wafer fabrication factory to be scheduled with two adjustment factors. 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 average cycle time and standard deviations. Besides, the fuzzy-neural system was also shown to be a Pareto optimal solution for scheduling jobs in a semiconductor manufacturing factory.