Possibilistic linear systems and their application to the linear regression model
Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
Optimal common due-date with completion time tolerance
Computers and Operations Research
A Case-Based Reasoning Approach for Due-Date Assignment in a Wafer Fabrication Factory
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
An intelligent hybrid system for wafer lot output time prediction
Advanced Engineering Informatics
Modeling and control for nonlinear structural systems via a NN-based approach
Expert Systems with Applications: An International Journal
International Journal of Systems Science
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
A collaborative demand forecasting process with event-based fuzzy judgements
Computers and Industrial Engineering
Manufacturing intelligence to forecast and reduce semiconductor cycle time
Journal of Intelligent Manufacturing
Forecasting the yield of a semiconductor product with a collaborative intelligence approach
Applied Soft Computing
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Predicting the cycle time of each job in a factory is an important task to the factory. However, it is not easy to deal with the uncertainty in the job cycle time. To cope with this problem and to effectively predict the job cycle time, an effective fuzzy collaborative forecasting approach is proposed in this study. The main difference between the proposed methodology and the existing methods is that the proposed methodology generates a fuzzy cycle time forecast in an effective way. In addition, the proposed method utilizes each round of fuzzy artificial neural network training to generate the upper and lower bounds of the job cycle time. The upper and lower bounds then serve as the basis for the subsequent collaboration. We collected the data of 120 jobs from a wafer fabrication factory to assess the effectiveness of the proposed method. The analysis results showed that the proposed fuzzy collaborative forecasting approach was indeed more efficient and accurate than some existing methods.