A Novel Fuzzy-Neural Slack-Diversifying Rule Based on Soft Computing Applications for Job Dispatching in a Wafer Fabrication Factory

This study proposes a slack-diversifying fuzzy-neural rule to improve job dispatching in a wafer fabrication factory. Several soft computing techniques, including fuzzy classification and artificial neural network prediction, have been applied in the proposed methodology. A highly effective fuzzy-neural approach is applied to estimate the remaining cycle time of a job. This research presents empirical evidence of the relationship between the estimation accuracy and the scheduling performance. Because dynamic maximization of the standard deviation of schedule slack has been shown to improve performance, this work applies such maximization to a slack-diversifying fuzzy-neural rule derived from a two-factor tailored nonlinear fluctuation smoothing rule for mean cycle time (2f-TNFSMCT). The effectiveness of the proposed rule was checked with a simulated case, which provided evidence of the rule’s effectiveness. The findings in this research point to several directions that can be exploited in the future

Guaranteed cost control of linear uncertain time-delay switched singular systems based on an LMI approach

This contribution considers the guaranteed cost control for a class of linear uncertain time-delay switched singular systems under arbitrary switching laws with a given quadratic performance index. Based on a Lyapunov function approach and a linear matrix inequality (LMI) technique, a sufficient condition on the existence of guaranteed cost state feedback controllers is derived, which ensures that the uncertain time-delay switched singular system is admissible and a desired level of cost index can be guaranteed. The design problem of guaranteed cost controller is turned into the solvable problem of a set of LMIs. A numerical example is given to show the effectiveness and feasibility of the presented method