A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using data mining to find patterns in genetic algorithm solutions to a job shop schedule
Computers and Industrial Engineering
A combined dispatching criteria approach to scheduling semiconductor manufacturing systems
Computers and Industrial Engineering
Computers and Industrial Engineering
Rule-based scheduling in wafer fabrication with due date-based objectives
Computers and Operations Research
Applied Computational Intelligence and Soft Computing - Special issue on Applied Neural Intelligence to Modeling, Control, and Management of Human Systems and Environments
Robotics and Computer-Integrated Manufacturing
International Journal of Fuzzy System Applications
International Journal of Intelligent Information Technologies
Computational Intelligence and Neuroscience
Hi-index | 0.00 |
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.