Non-dominated sorting genetic algorithm (NSGA-II) approach to the multi-objective economic statistical design of variable sampling interval $T^2$ control charts

Year 2015, Volume: 44 Issue: 1, 203 - 214, 01.02.2015

Asghar Seif Majide Sadeghifar

Abstract

$T^2$ control charts are used to primarily monitor the mean vector of quality characteristics of a process. Recent studies have shown that using variable sampling 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 $T^2$ 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.

Keywords

Hotelling’s T 2 control chart, Economic Statistical Design, NSGA-II Algorithm, Multiple-Objective Optimization, variable sampling interval (VSI) scheme

References

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