How to cite this paper
Madadi, M & Mahmoudzadeh, M. (2017). A fuzzy approach for statistical modeling of operators’ performance.Management Science Letters , 7(10), 487-496.
Refrences
Bajpai, N. (2009). Business statistics. Pearson Education India.
Bradshaw, C. W. (1983). A fuzzy set theoretic interpretation of economic control limits. European Journal of Operational Research, 13(4), 403-408.
Cheng, C. B. (2005). Fuzzy process control: construction of control charts with fuzzy numbers. Fuzzy sets and systems, 154(2), 287-303.
Chiu, J. E., & Chen, B. T. (2012). A Fuzzy System for VSI X-Bar Control Chart. International Jour-nal of Engineering and Technology, 4(4), 427.
Du, K. L., & Swamy, M. N. (2006). Neural networks in a softcomputing framework. Springer Science & Business Media.
Gülbay, M., & Kahraman, C. (2006). Development of fuzzy process control charts and fuzzy unnatu-ral pattern analyses. Computational statistics & data analysis, 51(1), 434-451.
Gülbay, M., & Kahraman, C. (2007). An alternative approach to fuzzy control charts: Direct fuzzy approach. Information sciences, 177(6), 1463-1480.
Gülbay, M., Kahraman, C., & Ruan, D. (2004). α‐Cut fuzzy control charts for linguistic da-ta. International Journal of Intelligent Systems, 19(12), 1173-1195.
Hsieh, K. L., Tong, L. I., & Wang, M. C. (2007). The application of control chart for defects and de-fect clustering in IC manufacturing based on fuzzy theory. Expert Systems with Applica-tions, 32(3), 765-776.
Kanagawa, A., Tamaki, F., & Ohta, H. (1993). Control charts for process average and variability based on linguistic data. The International Journal of Production Research, 31(4), 913-922.
Kandel, A., Martins, A., & Pacheco, R. (1995). Discussion: on the very real distinction between fuzzy and statistical methods. Technometrics, 37(3), 276-281.
Kuipers, B. (1989). Qualitative reasoning: modeling and simulation with incomplete knowledge. Automatica, 25(4), 571-585.
Lee, G. (2012). Advances in Intelligent Systems. Springer Science & Business Media.
Misra, K. B. (Ed.). (2008). Handbook of performability engineering. Springer Science & Business Media.
Montgomery, D. C. (2009). Introduction to statistical quality control. John Wiley & Sons (New York).
Oakland, J. S. (2007). Statistical process control. Routledge.
Pandurangan, A., & Varadharajan, R. (2011). Fuzzy multinomial control chart with variable sample size. International Journal of Engineering Science, 3.
Pearson, D. W., Steele, N. C., & Albrecht, R. (Eds.). (2011). Artificial Neural Nets and Genetic Algo-rithms: Proceedings of the International Conference in Roanne, France, 2003. Springer Science & Business Media.
Raz, T., & Wang, J. H. (1990). Probabilistic and membership approaches in the construction of control charts for linguistic data. Production Planning & Control, 1(3), 147-157.
Ross, T. J. (2004). Fuzzy logic with engineering applicationsJohn Wiley & Sons. Inc. New York, US.
Rowlands, H., & Wang, L. R. (2000). An approach of fuzzy logic evaluation and control in SPC. Quality and Reliability Engineering International, 16(2), 91-98.
Sivanandam, S. N., Sumathi, S., & Deepa, S. N. (2007). Introduction to fuzzy logic using MATLAB (Vol. 1). Berlin: Springer.
Sorooshian, S. (2013). Fuzzy approach to statistical control charts. Journal of Applied Mathemat-ics, 2013.
Starczewski, J. T. (2012). Advanced concepts in fuzzy logic and systems with membership uncertain-ty (Vol. 284). Springer.
Taleb, H., & Limam, M. (2002). On fuzzy and probabilistic control charts.International Journal of Production Research, 40(12), 2849-2863.
Tannock, J. D. T. (2003). A fuzzy control charting method for individuals.International Journal of Production Research, 41(5), 1017-1032.
Veerarajan, T. (2008). Probability, Statistics and Random Processes. Tata McGraw-Hill.
Walker, H. F., ElshennawY, A. K., Gupta, B. C., & Vaughn, M. M. (2012).The certified quality in-spector handbook. ASQ Quality Press.
Xie, M., Goh, T. N., & Kuralmani, V. (2012). Statistical models and control charts for high-quality processes. Springer Science & Business Media.
Zimmermann, H. J. (2011). Fuzzy set theory—and its applications. Springer Science & Business Me-dia.
Bradshaw, C. W. (1983). A fuzzy set theoretic interpretation of economic control limits. European Journal of Operational Research, 13(4), 403-408.
Cheng, C. B. (2005). Fuzzy process control: construction of control charts with fuzzy numbers. Fuzzy sets and systems, 154(2), 287-303.
Chiu, J. E., & Chen, B. T. (2012). A Fuzzy System for VSI X-Bar Control Chart. International Jour-nal of Engineering and Technology, 4(4), 427.
Du, K. L., & Swamy, M. N. (2006). Neural networks in a softcomputing framework. Springer Science & Business Media.
Gülbay, M., & Kahraman, C. (2006). Development of fuzzy process control charts and fuzzy unnatu-ral pattern analyses. Computational statistics & data analysis, 51(1), 434-451.
Gülbay, M., & Kahraman, C. (2007). An alternative approach to fuzzy control charts: Direct fuzzy approach. Information sciences, 177(6), 1463-1480.
Gülbay, M., Kahraman, C., & Ruan, D. (2004). α‐Cut fuzzy control charts for linguistic da-ta. International Journal of Intelligent Systems, 19(12), 1173-1195.
Hsieh, K. L., Tong, L. I., & Wang, M. C. (2007). The application of control chart for defects and de-fect clustering in IC manufacturing based on fuzzy theory. Expert Systems with Applica-tions, 32(3), 765-776.
Kanagawa, A., Tamaki, F., & Ohta, H. (1993). Control charts for process average and variability based on linguistic data. The International Journal of Production Research, 31(4), 913-922.
Kandel, A., Martins, A., & Pacheco, R. (1995). Discussion: on the very real distinction between fuzzy and statistical methods. Technometrics, 37(3), 276-281.
Kuipers, B. (1989). Qualitative reasoning: modeling and simulation with incomplete knowledge. Automatica, 25(4), 571-585.
Lee, G. (2012). Advances in Intelligent Systems. Springer Science & Business Media.
Misra, K. B. (Ed.). (2008). Handbook of performability engineering. Springer Science & Business Media.
Montgomery, D. C. (2009). Introduction to statistical quality control. John Wiley & Sons (New York).
Oakland, J. S. (2007). Statistical process control. Routledge.
Pandurangan, A., & Varadharajan, R. (2011). Fuzzy multinomial control chart with variable sample size. International Journal of Engineering Science, 3.
Pearson, D. W., Steele, N. C., & Albrecht, R. (Eds.). (2011). Artificial Neural Nets and Genetic Algo-rithms: Proceedings of the International Conference in Roanne, France, 2003. Springer Science & Business Media.
Raz, T., & Wang, J. H. (1990). Probabilistic and membership approaches in the construction of control charts for linguistic data. Production Planning & Control, 1(3), 147-157.
Ross, T. J. (2004). Fuzzy logic with engineering applicationsJohn Wiley & Sons. Inc. New York, US.
Rowlands, H., & Wang, L. R. (2000). An approach of fuzzy logic evaluation and control in SPC. Quality and Reliability Engineering International, 16(2), 91-98.
Sivanandam, S. N., Sumathi, S., & Deepa, S. N. (2007). Introduction to fuzzy logic using MATLAB (Vol. 1). Berlin: Springer.
Sorooshian, S. (2013). Fuzzy approach to statistical control charts. Journal of Applied Mathemat-ics, 2013.
Starczewski, J. T. (2012). Advanced concepts in fuzzy logic and systems with membership uncertain-ty (Vol. 284). Springer.
Taleb, H., & Limam, M. (2002). On fuzzy and probabilistic control charts.International Journal of Production Research, 40(12), 2849-2863.
Tannock, J. D. T. (2003). A fuzzy control charting method for individuals.International Journal of Production Research, 41(5), 1017-1032.
Veerarajan, T. (2008). Probability, Statistics and Random Processes. Tata McGraw-Hill.
Walker, H. F., ElshennawY, A. K., Gupta, B. C., & Vaughn, M. M. (2012).The certified quality in-spector handbook. ASQ Quality Press.
Xie, M., Goh, T. N., & Kuralmani, V. (2012). Statistical models and control charts for high-quality processes. Springer Science & Business Media.
Zimmermann, H. J. (2011). Fuzzy set theory—and its applications. Springer Science & Business Me-dia.