Developing new Methods to Monitor the Fuzzy Logistic Regression Profiles in Phase II (A case study in health-care)
الموضوعات :
Mona Gharegozloo
1
,
Reza Kamranrad
2
1 - Department of Industrial Engineering, Faculty of engineering, Semnan university, Semnan, Iran
2 - Department of industrial engineering, faculty of engineering, Semnan university, Semnan, Iran
الکلمات المفتاحية: Fuzzy Quality Profile, FEWMA Fuzzy Statistics, Hotelling, s F T^2, ARL,
ملخص المقالة :
In real quality control applications, the performance of a process or the quality of a product is described by the relationship between a non-metric response variable and one or more control variables. Furthermore, the quality characteristic of a product or process is vague, unreliable, and linguistic and cannot be accurately expressed in most practical applications. This study was carried out aimed to provide a method for monitoring the fuzzy logistic regression profile in Phase II. In these circumstances, there is a need for special diagrams to monitor the performance of this fuzzy data. To this aim, some powerful control charts including Fuzzy exponentially weighted moving average (FEWMA), fuzzy T2 (FT2) control chart have been developed. In addition, to show the performance of the proposed control charts, the fuzzy hypothesis test along with average Run Length (ARL) criterion is used in Phase II. In addition, to show the efficiency of the proposed control chart in real applications, a real case study in health-care has been applied.
1] M.Kosha, (2011) Development of methods for monitoring extended linear patterns based on generalized linear models, end quote senior industrial engineer, shahed university.
[2] J. Lovergrov, C. Sherlaw-Jahnson, O. Valencia, S. Gallivan, (1999) Monitoring the performance of cardiac surgeons, Journal Research Society, 5, 685-689.
[3]J. Lovergrov, O. Valencia, T. Treasure, C. Sherlaw-Jahnson, S. Gallivan, Monitoring the result of cardiac surgeryby variable life-adjusted display, The Lancet, 350 (1997), 1128-1130.
[4]Woodall, W. H. and Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31: 376-386.
[5] Montgomery, D.C., (2005) Introduction to statistical quality control, 5th edition, John Wiley &Sons [6] Woodall, W.H., Spitzner, D.J., Montgomery, D.C. and Gupta, S. (2004), .Using control charts to monitor process and product quality profiles. Journal of Quality Technology, 36: 309- 320.
[7] Woodall, W.H.,."Current Research on Profile Monitoring"., Revista Producão,vol.12, no.17,pp.420- 425, 2007
[8] C. B. Cheng, Fuzzy process control: Construction of control charts with fuzzy numbers, Fuzzy Sets and Systems,154(2) (2005), 287-303.
[9] M., Noorossana, R.Amiri (2008), improving monitoring of linear profiles in phase‖ . amir kabir research scientific journal 19-27.
[10] McNeese, William (July 2006). "Over-controlling a Process: The Funnel Experiment". BPI Consulting, LLC. Retrieved 2010-03-17.
[11] Shang Y., Tsung F. and Zou C. (2011). Phase-II profile monitoring with binary data and random predictors. Journal of Quality Technology, 43:196-208.
[12] Paynabar K., Jin J. and Yeh A.B. (2012). Phase I risk-adjusted control charts for monitoring surgical performance by considering categorical covariates. Journal of Quality Technology, 44: 39-53.
[13] Amiri, A., Eyvazian, M., Zou, C., and Noorossana, R. (2012). A parameters reduction method for monitoring multiple linear regression profiles. The International Journal of Advanced Manufacturing Technology, 58: 621-629.
[14] Saghaei A., Rezazadeh-Saghaei M., Noorossana R. and Dorri M. (2012). Phase II logistic profile monitoring. International Journal of Industrial Engineering & Production Research, 23: 291-299.
[15] Soleymanian, M. E., M. Khedmati, and H. Mahlooji. "Phase II monitoring of binary response profiles." Scientia Iranica. Transaction E, Industrial Engineering 20.6 (2013): 2238.
[16] Koosha M. and Amiri A. (2013). Generalized linear mixed model for monitoring autocorrelated logistic regression profiles. International Journal of Advanced Manufacturing Technology, 64:487–495.
[17] Yeh A.B., Huwang L. and Li Y.M. (2009). Profile monitoring for a binary response. IIE Transactions, 41: 931-941.
[18]A.Amiri, M.Emani (2014) development of a method for improving monitoring of logistics profiles in phase ‖. journal of engineering and quality management 110-103.
[19] G. Moghadam, G. A. Raissi Ardali, V. Amirzadeh Devloping New Methods to Monitor Phase II Fuzzy Linear Profiles, Iranian Journal of Fuzzy Systems Vol. 12, No. 4, (2015) pp. 59-77
[20]R.Kamran rad.(2017), monitoring of multiple classifiers based on agreed tables, dissertation PhD in Industrial Engineering,shahed university.
[21] A. Rezaeifar, B. Sadeghpour Gildeh and G. R. Mohtashami Borzadaran Risk-adjusted control charts based on LR-fuzzy data Iranian Journal of Fuzzy Systems Volume 17, Number 5, (202e0), pp. 69-80
[22] R.M. Dom, R. Zain, S. Abdul Kareem, B. Abidin, An adaptive fuzzy regression model for the prediction of dichotomous response variables, in: 15th Conference on Computational Science and Applications, Malaysia, 2007, pp. 14–19.
[23] H.J. Zimmerman, Fuzzy Set Theory and its Applications, Kluwer Academic, Boston, 1991.
[24] R. Viertl, Statistical Methods for Fuzzy Data, John Wiley and Sons, Austria, 2011.
[25] R. Korner and W. Nather, Linear regression with random fuzzy variables: extended classical estimates, best linear estimates, least squares estimates, J. Information Sciences, 109 (1998), 95-118.
[26] J.J. Buckley, T. Feuring, Linear and non-linear fuzzy regression: evolutionary algorithm solutions, Fuzzy Sets and Systems 112 (2000) 381–394.
[27] J.J. Buckley, T. Feuring, Y. Hayashi, Multivariate non-linear fuzzy regression: an evolutionary algorithm approach, International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems 7 (1999) 83–98.
[28] A. Celmins, A practical approach to nonlinear fuzzy regression, SIAM Journal on Scientific and Statistical Computing 12 (1991) 521–546.
[29] H. Tanaka, S. Uejima, K. Asai, Linear regression analysis with fuzzy model, IEEE Transactions on Systems, Man and Cybernetics 12 (1982) 903–907.
[30] P. Diamond, Least squares fitting of several fuzzy variables, in: Proc. of the Second IFSA Congress, Tokyo, 1987, pp. 20–25.
[31] S. Pourahmad, S.M.T. Ayatollahi, S.M. Taheri, Fuzzy logistic regression, a new posssibilistic regression and its application in clinical vague status, Iranian Journal of Fuzzy Systems 8 (2011) 1–17.
[32] H.C. Wu, Linear regression analysis for fuzzy input and output data using the extension principle, Computers & Mathematics with Applications 45(2003) 1849–1859.
[33] A. Celmins, Least squares model fitting to fuzzy vector data, Fuzzy Sets and Systems 22 (1987) 260–269. [34] P. Diamond, H. Tanaka, Fuzzy regression analysis, in: R. Slowinski (Ed.), Fuzzy Sets in Decision Analysis, Operations Research and Statistics, Kluwer,Academic Publishers, USA, MA, 1998, pp. 349–387.
[35] P. Diamond, R. Korner, Extended fuzzy linear models and least squares estimates, Computers and Mathematics with Applications 33 (1997) 15–32.
[36] R. Xu, C. Li, Multidimensional least-squares fitting with fuzzy model, Fuzzy Sets and Systems 119 (2001) 215–223.
[37] Chi-Bin Cheng, Fuzzy process control: construction of control charts with fuzzy numbers, Fuzzy sets and systems, 154 (2005) 287-303.
[38] B. S. Gildeh, D. Gien, La distance-Dp, q et le coe_cient de correlation entre deux variables aleatoires oues, Encontres Francophones Sur la Logique Floue et Ses Applications LFA0 1, 2001.
[39] R. Korner and W. Nather, Linear regression with random fuzzy variables: extended classicalestimates, best linear estimates, least squares estimates, J. Information Sciences, 109 (1998),95-118.
[40] R. Viertl, Statistical Methods for Fuzzy Data, John Wiley and Sons, Austria, 2011.
