How to cite this paper
Madadi, M & Mahmoudzadeh, M. (2017). A fuzzy development for attribute control chart with Monte Carlo simulation method.Management Science Letters , 7(11), 555-564.
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.
Cox, E. (2005). Fuzzy modeling and genetic algorithms for data mining and exploration. Elsevier.
Du, K. L., & Swamy, M. N. (2006). Neural networks in a softcomputing framework. Springer Science & Business Media.
El-Shal, S. M., & Morris, A. S. (2000). A fuzzy rule-based algorithm to improve the performance of statistical process control in quality systems.Journal of Intelligent & Fuzzy Systems, 9(3, 4), 207-223.
Franceschini, F., & Romano, D. (1999). Control chart for linguistic variables: a method based on the use of linguistic quantifiers. International Journal of Production Research, 37(16), 3791-3801.
Fryman, M. A. (2002). Quality and process improvement. Cengage Learning.
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.
Gülbay, M., & Kahraman, C. (2007). An alternative approach to fuzzy control charts: Direct fuzzy approach. Information sciences, 177(6), 1463-1480.
Kahraman, C. (Ed.). (2006). Fuzzy applications in industrial engineering (Vol. 201). Heidelberg: Springer.
Kahraman, C., Tolga, E., & Ulukan, Z. (1995, October). Using triangular fuzzy numbers in the tests of control charts for unnatural patterns. In Emerging Technologies and Factory Automation, 1995. ETFA'95, Proceedings., 1995 INRIA/IEEE Symposium on (Vol. 3, pp. 291-298). IEEE.
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.
Kim, S. H. (1992). Statistics and decisions: An introduction to foundations. CRC Press.
Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo method (Vol. 10). John Wiley & Sons.
Lighter, D. E., & Fair, D. C. (2000). Principles and methods of quality management in health care. Jones & Bartlett Learning.
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).
Pandurangan, A., & Varadharajan, R. (2011). Fuzzy multinomial control chart with variable sample size. International Journal of Engineering Science, 3.
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. (2009). Fuzzy logic with engineering applications. John Wiley & Sons.
Shu, M. H., & Wu, H. C. (2010). Monitoring imprecise fraction of nonconforming items using p con-trol charts. Journal of Applied Statistics,37(8), 1283-1297.
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.
Timm, N. H. (2002). Applied multivariate analysis. Springer Verlag.
Wang, J. H., & RAZ, T. (1990). On the construction of control charts using linguistic variables. The International Journal of Production Research, 28(3), 477-487.
Webber, L., & Wallace, M. (2011). Quality control for dummies. John Wiley & Sons.
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.
Cox, E. (2005). Fuzzy modeling and genetic algorithms for data mining and exploration. Elsevier.
Du, K. L., & Swamy, M. N. (2006). Neural networks in a softcomputing framework. Springer Science & Business Media.
El-Shal, S. M., & Morris, A. S. (2000). A fuzzy rule-based algorithm to improve the performance of statistical process control in quality systems.Journal of Intelligent & Fuzzy Systems, 9(3, 4), 207-223.
Franceschini, F., & Romano, D. (1999). Control chart for linguistic variables: a method based on the use of linguistic quantifiers. International Journal of Production Research, 37(16), 3791-3801.
Fryman, M. A. (2002). Quality and process improvement. Cengage Learning.
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.
Gülbay, M., & Kahraman, C. (2007). An alternative approach to fuzzy control charts: Direct fuzzy approach. Information sciences, 177(6), 1463-1480.
Kahraman, C. (Ed.). (2006). Fuzzy applications in industrial engineering (Vol. 201). Heidelberg: Springer.
Kahraman, C., Tolga, E., & Ulukan, Z. (1995, October). Using triangular fuzzy numbers in the tests of control charts for unnatural patterns. In Emerging Technologies and Factory Automation, 1995. ETFA'95, Proceedings., 1995 INRIA/IEEE Symposium on (Vol. 3, pp. 291-298). IEEE.
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.
Kim, S. H. (1992). Statistics and decisions: An introduction to foundations. CRC Press.
Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo method (Vol. 10). John Wiley & Sons.
Lighter, D. E., & Fair, D. C. (2000). Principles and methods of quality management in health care. Jones & Bartlett Learning.
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).
Pandurangan, A., & Varadharajan, R. (2011). Fuzzy multinomial control chart with variable sample size. International Journal of Engineering Science, 3.
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. (2009). Fuzzy logic with engineering applications. John Wiley & Sons.
Shu, M. H., & Wu, H. C. (2010). Monitoring imprecise fraction of nonconforming items using p con-trol charts. Journal of Applied Statistics,37(8), 1283-1297.
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.
Timm, N. H. (2002). Applied multivariate analysis. Springer Verlag.
Wang, J. H., & RAZ, T. (1990). On the construction of control charts using linguistic variables. The International Journal of Production Research, 28(3), 477-487.
Webber, L., & Wallace, M. (2011). Quality control for dummies. John Wiley & Sons.
Zimmermann, H. J. (2011). Fuzzy set theory—and its applications. Springer Science & Business Me-dia.