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AuthorAbbasi S.A.
Available date2020-04-09T07:35:01Z
Publication Date2019
Publication NameQuality and Reliability Engineering International
ResourceScopus
ISSN7488017
URIhttp://dx.doi.org/10.1002/qre.2463
URIhttp://hdl.handle.net/10576/13927
AbstractControl charts, based on ranked set sampling schemes, had been proposed recently for efficient monitoring of process location. All the proposals in the literature are based on the ideal assumption of normally distributed quality characteristics. No study as of yet investigated the performance of location charts based on ranked set sampling for non-normal processes. In this study, we investigated the location (Formula presented.) chart based on simple random sampling (SRS) and three well-known rank-based schemes ie, ranked set sampling (RSS), median ranked set sampling (MRSS), and extreme ranked set sampling (ERSS), considering normal and a variety of non-normal parent distributions. Both heavy-tailed symmetric and skewed cases have been considered in this study. The performance of the charts is evaluated using average run length (ARL), extra quadratic loss (EQL), and relative ARL (RARL) measures. A real life example is also presented that details the monitoring of pH levels in water for an experiment conducted to study the reproduction of Mysids. The study will help quality practitioners to choose the (Formula presented.) chart based on an efficient sampling scheme for normal and non-normal processes. - 2019 John Wiley & Sons, Ltd.
SponsorThe author is thankful to the editor and referees for their constructive comments that lead to improvements in the paper. The author would also like to acknowledge Qatar University for providing excellent research facilities.
Languageen
PublisherJohn Wiley and Sons Ltd
Subjectaverage run length
extra quadratic loss
location parameter
non-normality
pH level
ranked set sampling
TitleLocation charts based on ranked set sampling for normal and non-normal processes
TypeArticle
Pagination1603-1620
Issue Number6
Volume Number35
dc.accessType Abstract Only


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