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Parchami, A., Iranmanesh, H., Sadeghpour Gildeh, B.. (1400). Monte Carlo statistical test for fuzzy quality. نشریه علمی دانشگاه سیستان و بلوچستان, 19(1), 115-124. doi: 10.22111/ijfs.2022.6555
A. Parchami; H. Iranmanesh; B. Sadeghpour Gildeh. "Monte Carlo statistical test for fuzzy quality". نشریه علمی دانشگاه سیستان و بلوچستان, 19, 1, 1400, 115-124. doi: 10.22111/ijfs.2022.6555
Parchami, A., Iranmanesh, H., Sadeghpour Gildeh, B.. (1400). 'Monte Carlo statistical test for fuzzy quality', نشریه علمی دانشگاه سیستان و بلوچستان, 19(1), pp. 115-124. doi: 10.22111/ijfs.2022.6555
Parchami, A., Iranmanesh, H., Sadeghpour Gildeh, B.. Monte Carlo statistical test for fuzzy quality. نشریه علمی دانشگاه سیستان و بلوچستان, 1400; 19(1): 115-124. doi: 10.22111/ijfs.2022.6555

Monte Carlo statistical test for fuzzy quality

Iranian Journal of Fuzzy Systems
مقاله 10، دوره 19، شماره 1، فروردین و اردیبهشت 2022، صفحه 115-124    اصل مقاله (287.59 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2022.6555
نویسندگان
A. Parchami* 1؛ H. Iranmanesh2؛ B. Sadeghpour Gildeh2
1Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
2Department of Statistics, Factually of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده
Testing the capability of a productive process on the basis of the flexible fuzzy quality  using Yongting's index is proposed in this paper by the Monte Carlo simulation.
The theoretical approach and detailed steps of an algorithm are given to simulate the critical-value-based and also p-value-based approaches to statistical testing fuzzy quality. Also, the probability of type \textit{II} error of the quality test simulated by Monte Carlo  approach. Moreover, a real-world case study is provided to show the performance of the proposed algorithm for triangular and trapezoidal fuzzy qualities.
کلیدواژه‌ها
Testing hypotheses؛ p-value؛ the probability of type II error؛ fuzzy quality؛ capability index؛ Monte Carlo
مراجع
[1] V. Amirzadeh, M. Mashinchi, A. Parchami, Construction of p-charts using degree of nonconformity, Information Sciences, 179(2) (2009), 150-160.

[2] A. C. Cullen, H. C. Frey, C. H. Frey, Probabilistic techniques in exposure assessment, Springer Science and Business Media, New York, 1st edition, 1999.

[3] D. C. Montgomery, Introduction to statistical quality control, John Wiley and Sons, New York, 2001.

[4] A. Parchami, M. Mashinchi, A new generation of process capability indices, Journal of Applied Statistics, 37(1) (2010), 77-89.

[5] A. Parchami, B. Sadeghpour-Gildeh, M. Mashinchi, Why fuzzy quality?, International Journal for Quality Research, 10(3) (2016), 457-470.

[6] W. L. Pearn, S. Kotz, Encyclopedia and handbook of process capability indices: A comprehensive exposition of quality control measures, World Scientific, Singapore, 2006.

[7] B. Sadeghpour Gildeh, Comparison of Cp, Cpk and Cp-tilde process capability indices in the case of measurement error occurrence, IFSA World Congress, Istanbul, Turkey, (2003), 563-567.

[8] L. Scrucca, qcc: An R package for quality control charting and statistical process control, R. News, 4(1) (2004), 11-17.

[9] C. Yongting, Fuzzy quality and analysis on fuzzy probability, Fuzzy Sets and Systems, 83 (1996), 283-290.

[10] L. A. Zadeh, Probability measures of fuzzy events, Journal of Mathematical Analysis and Applications, 23(2) (1968), 421-427.

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