Fuzzy testing of operating performance index based on confidence intervals

Kuen-Suan Chen ()
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Kuen-Suan Chen: National Chin-Yi University of Technology

Annals of Operations Research, 2022, vol. 311, issue 1, No 3, 19-33

Abstract: Abstract The operating performance index (OPI) was developed by Chen and Yang (J Comput Appl Math 343:737–747, 2018) from the Six Sigma process quality index. The fact that OPIs include unknown parameters means that they must be formulated using estimates based on sample data. Unfortunately, cost and effectiveness considerations in practice have led to sample size limitation and measurement uncertainty. In this study, we sought to enhance testing accuracy and overcome the uncertainties in measurement by applying confidence intervals of OPI to derive a fuzzy number and membership function for OPI. We developed a one-tailed fuzzy test method to determine whether performance reaches the required level. We also developed a two-tailed fuzzy testing method based on two OPIs to serve as a verification model for the effectiveness of improvement measures. Both fuzzy testing methods are proposed based on confidence intervals of the indices to reduce the risk of misjudgment caused by sampling errors and enhance testing accuracy.

Keywords: Operating performance index; Confidence interval; α-Cuts; Membership function of fuzzy number; Fuzzy testing (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s10479-019-03242-x

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