Hotelling T2 chart using the generalized multiple dependent state sampling scheme
| Autor: | Maria Nela Pastuizaca, Joseph Leon, Sandra García-Bustos |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science Strategy and Management Process (computing) Sampling (statistics) 02 engineering and technology 01 natural sciences General Business Management and Accounting 010104 statistics & probability 020901 industrial engineering & automation Chart Genetic algorithm Hotelling's T-squared distribution Control chart Sensitivity (control systems) 0101 mathematics Algorithm Statistic |
| Zdroj: | International Journal of Quality & Reliability Management. 38:1265-1277 |
| ISSN: | 0265-671X |
| DOI: | 10.1108/ijqrm-03-2020-0084 |
| Popis: | PurposeThis research proposes a multivariate control chart, whose parameters are optimized using genetic algorithms (GA) in order to accelerate the detection of a change in the vector of means.Design/methodology/approachThis chart is based on a variation of the Hotelling T2 chart using a sampling scheme called generalized multiple dependent state sampling. For the analysis of performances of this chart, the out-of-control average run length (ARL) values were used for different scenarios. In this comparison, it was considered the classic Hotelling T2 chart and the T2 chart using the scheme called multiple dependent state sampling.FindingsIt was observed that the new chart with its optimized parameters is more efficient to detect an out-of-control process. Additionally, a sensitivity analysis was performed, and it was concluded that the best yields are obtained when the change to be considered in the optimization is small. An application in the resolution of a real problem is given.Originality/valueIn this research, a multivariate control chart is proposed based on the Hotelling T2 statistic but adding a sampling scheme. This makes this control chart more efficient than the classic T2 chart because the new chart not only uses the current information of the T2 statistic but also conditions the decision to consider a process as “in- control” on the statistic's previous information. The practitioner can obtain the optimal parameters of this new chart through a friendly program developed by the authors. |
| Databáze: | OpenAIRE |
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