A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal common due-date with completion time tolerance
Computers and Operations Research
An intelligent hybrid system for wafer lot output time prediction
Advanced Engineering Informatics
An intelligent mechanism for lot output time prediction and achievability evaluation in a wafer fab
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
A look-ahead fuzzy back propagation network for lot output time series prediction in a wafer fab
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Predicting Wafer-Lot Output Time With a Hybrid FCM–FBPN Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A collaborative fuzzy-neural approach for long-term load forecasting in Taiwan
Computers and Industrial Engineering
Forecasting the yield of a semiconductor product with a collaborative intelligence approach
Applied Soft Computing
A PCA-FBPN Approach for Job Cycle Time Estimation in a Wafer Fabrication Factory
International Journal of Fuzzy System Applications
A collaborative and artificial intelligence approach for semiconductor cost forecasting
Computers and Industrial Engineering
Hi-index | 0.00 |
To enhance the performance of the internal due date assignment in a wafer fab even further, this study incorporated the fuzzy c-means-back propagation network (FCM-BPN) approach with a nonlinear programming model. In the proposed methodology, the jobs are first classified into several categories by fuzzy c-means. Then, an individual back propagation network is constructed for each category to predict the completion time of the jobs. Subsequently, an individual nonlinear programming model is constructed for each back propagation network to adjust the connection weights in the back propagation network, allowing us to determine the internal due dates of the jobs in the category. The nonlinear programming model is finally converted into a goal programming problem that can be solved with existing optimization software. According to the experimental results, the proposed methodology outperforms the baseline multiple linear regression (MLR) approach by 24% in predicting the job completion/cycle times. In addition, the proposed methodology also guarantees that all jobs can be finished before the established internal due dates, without adding too large a fudge factor, and without sacrificing the accuracy of the completion/cycle time forecasts.