This paper constructs a data-mining-based localized scheduling system to improve the performance of scheduling jobs in a wafer fabrication factory (wafer fab). The system is modified from Chen's tailored nonlinear fluctuation smoothing (TNFS) rule with some innovative treatments. First, the remaining cycle time of a job to be scheduled is estimated with a data mining approach instead to improve the accuracy. Second, in the original TNFS rule, the adjustable factor is static, while in this system it becomes dynamic. Third, the adjustable factor is also dependent on the stages of the jobs to be scheduled. Namely, the TNFS rule is localized. To evaluate the effectiveness of the proposed methodology, production simulation was also applied in this study. According to experimental results, the proposed methodology outperformed some existing approaches in reducing the average cycle time and cycle time variation.
|Number of pages||8|
|Journal||International Review on Computers and Software|
|State||Published - 1 Jan 2010|
- Data mining
- Wafer fab