Non-dominated sorting genetic algorithm (NSGA-II) approach to the multi-objective economic statistical design of variable sampling interval T2 control charts

Abstract

T2 control charts are used to primarily monitor the mean vector of quality char- acteristics of a process. Recent studies have shown that using variable sam- pling interval (VSI) schemes results in charts with more statistical power for detecting small to moderate shifts in the process mean vector. In this study, we have presented a multiple-objective economic statistical design of VSI T2 control chart when the in-control process mean vector and process covariance matrix are unknown. Then we exert to find the Pareto-optimal designs in which the two objectives are minimized simultaneously by using the Non-dominated sorting genetic algorithm. Through an illustrative example, the advantages of the proposed approach is shown by providing a list of viable optimal solutions and graphical representations, thereby bolding the advantage of flexibility and adaptability. 2000 AMS Classifi cation: