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Dergiler / Hacettepe Journal of Mathematics and Statistics / 2019 / Cilt: 48 - Sayı: 2

Interval type-2 fuzzy c-Control charts using ranking methods

Hatice ERCAN TEKŞEN
Ahmet Sermet ANAGÜN
Sayfa
510–520
Yayın Tarihi
2019-04-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This study introduces ranking methods as new approach to generate interval type-2 fuzzy control charts, which is a different field.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Control charts are important for process or product because they provide information about the control situation of process and product.
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Temel Bulgular & Veri:In the literature, there are some fuzzy control charts with type-1 fuzzy sets but there are few studies about fuzzy control charts regarding the cases where the data are expressed by type-2 fuzzy sets.
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Sonuç & Katkı:This study introduces ranking methods as new approach to generate interval type-2 fuzzy control charts, which is a different field.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Interval type-2 trapezoidal fuzzy sets · Fuzzy control charts · c-Control charts

Abstract

Control charts are important for process or product because they provide information about the control situation of process and product. Because of this feature, control charts are used in many fields. Information about the product and/or process, which is under control or not, can be provided by looking the control charts. Fuzzy numbers are used to reduce information losses in operations with crisp numbers. In control charts applications, especially for qualitative control charts, the fuzzy set theory reduces the information losses and provide more flexible decision-making process. In the literature, there are some fuzzy control charts with type-1 fuzzy sets but there are few studies about fuzzy control charts regarding the cases where the data are expressed by type-2 fuzzy sets. The purpose of the study is to create an innovation using the ranking methods, which has not used for control charts in accessible literature, for the fuzzy control charts with interval type-2 fuzzy sets. The fuzzy results are compared with the crisp results. This study introduces ranking methods as new approach to generate interval type-2 fuzzy control charts, which is a different field.

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Bu Makaleye Atıf Yap

TEKŞEN, H. E., ANAGÜN, A. S. (2019). Interval type-2 fuzzy c-Control charts using ranking methods. Hacettepe Journal of Mathematics and Statistics, Cilt: 48 - Sayı: 2, 510–520. https://ajindex.com/articles/669386

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Kaynakça (17)

  1. Alaeddini, A., Ghazanfari, M., and Nayeri, M. A. A hybrid fuzzy-statistical clustering approach for estimating the time of changes in fixed and variable sampling control charts. Information Sciences, 179(11):1769-1784. Including Special Issue on Chance Discovery, 2009.
  2. Asai, K. Fuzzy Systems for Management. IOS Press,
  3. Amsterdam, The Netherlands, 1st edition, 1995.
  4. Buckley, J. Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3):233-247, 1985.
  5. Chen, S.-M., Yang, M.-W., Lee, L.-W., and Yang, S.-W. Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets. Expert Systems with Applications, 39(5):5295-5308, 2012.
  6. Cheng, C.-B. Fuzzy process control: construction of control charts with fuzzy numbers. Fuzzy Sets and Systems, 154(2):287-303, 2005.
  7. Faraz, A. and Shapiro, A. F. An application of fuzzy random variables to control charts. Fuzzy Sets and Systems, 161(20):2684–2694. Theme: Games, Optimization and Discrete Structures, 2010.
  8. Gülbay, M. and Kahraman, C. Development of fuzzy process control charts and fuzzy unnatural pattern analyses. Computational Statistics & Data Analysis, 51(1):434-451. The Fuzzy Approach to Statistical Analysis, 2006.
  9. Gülbay, M. and Kahraman, C. An alternative approach to fuzzy control charts: Direct fuzzy approach. Information Sciences, 177(6):1463-1480, 2007.
  10. Gülbay, M., Kahraman, C., and Ruan, D. α-cut fuzzy control charts for linguistic data. International Journal of Intelligent Systems, 19(12):1173-1195, 2004.
  11. Kanagawa, A., Tamaki, F., and Ohta, H. Control charts for process average and variability based on linguistic data. International Journal of Production Research, 31(4):913-922, 1993.
  12. Kaya, I. and Kahraman, C. Process capability analyses based on fuzzy measurements and fuzzy control charts. Expert Systems with Applications, 38(4):3172-3184, 2011.
  13. Laviolette, M., Seaman, J. W., Barrett, J. D., and Woodall, W. H. A probabilistic and statistical view of fuzzy methods. Technometrics, 37(3):249-261, 1995.
  14. Mendel, J. Uncertain rule-based fuzzy systems: introduction and new directions. Springer, Cham, Switzerland, 2001.
  15. Qin, J. and Liu, X. Multi-attribute group decision making using combined ranking value under interval type-2 fuzzy environment. Information Sciences, 297:293-315, 2015.
  16. Raz, T. and Wang, J.-H. Probabilistic and membership approaches in the construction of control charts for linguistic data. Production Planning & Control, 1(3):147-157, 1990.
  17. Şentürk, S. and Antucheviciene, J. I