A bi-criteria nonlinear fluctuation smoothing rule incorporating the SOM–FBPN remaining cycle time estimator for scheduling a wafer fab—a simulation study

Abstract

This paper proposes a bi-criteria nonlinear fluctuation smoothing rule to further improve the performance of job scheduling in a wafer fabrication factory (wafer fab). The rule is based on the well-known fluctuation smoothing rules. First, the remaining cycle time of a job is estimated by applying the self-organization map–fuzzy back propagation network approach to improve the estimation accuracy. Second, two nonlinear forms of the fluctuation smoothing rules are obtained to enhance the balance and responsiveness. Third, the two nonlinear fluctuation smoothing rules are merged into a bi-criteria rule for considering two performance measures (average cycle time and cycle time variation) at the same time. Finally, the content of the bi-criteria rule can be tailored for the wafer fab and be scheduled with an adjustable factor. To evaluate the effectiveness of the proposed methodology, a production simulation was conducted. According to the experimental results, the proposed methodology outperformed some of the existing approaches by reducing the average cycle time and cycle time variation at the same time. In addition, the experimental results showed that the bi-criteria rule made it possible to improve one performance measure without raising the expense of another one.