How to cite this paper
Sasikala, P & Rao, P. (2023). Ranking fuzzy numbers by volume of solid of revolution of membership function about axis of support.Decision Science Letters , 12(4), 697-710.
Refrences
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Abbasbandy, S., & Hajjari, T. (2009). A new approach for ranking of trapezoidal fuzzy numbers. Computers & Mathematics with Applications, 57(3), 413–419.
Asady, B. (2011). Revision of distance minimization method for ranking of fuzzy numbers. Applied Mathematical Modelling, 35(3), 1306–1313.
Asady, B., & Zendehnam, A. (2007). Ranking fuzzy numbers by distance minimization. Applied Mathematical Modelling, 31(11), 2589–2598.
Chen, S., & Chen, J. (2009). Fuzzy risk analysis is based on ranking generalized fuzzy numbers with different heights and spreads. Expert Systems with Applications, 36(3), 6833–6842.
Chen, S., & Chen, S. (2007). Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Applied Intelligence, 26(1), 1–11.
Chen, S., Munif, A., Chen, G., Liu, H., & Kuo, B. (2012). Fuzzy risk analysis is based on ranking generalized fuzzy numbers with different left and right heights. Expert Systems with Applications, 39(7), 6320–6334.
Chen, S., & Sanguansat, K. (2011). Analyzing fuzzy risk based on a new fuzzy ranking method between generalized fuzzy numbers. Expert Systems with Applications, 38(3), 2163–2171.
Cheng, C. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems, 95(3), 307–317.
Chu, T., & Tsao, C. (2002). Ranking fuzzy numbers with an area between the centroid point and original point. Computers & Mathematics with Applications, 43(1–2), 111–117.
Chutia, R. (2017). Ranking of fuzzy numbers by using value and angle in the epsilon-deviation degree method. Applied Soft Computing, 60, 706–721.
De Hierro, A. F. R. L., Roldán, C., & Herrera, F. (2018). On a new methodology for ranking fuzzy numbers and its application to real economic data. Fuzzy Sets and Systems, 353, 86–110.
Dombi, J., & Jónás, T. (2020). Ranking trapezoidal fuzzy numbers using a parametric relation pair. Fuzzy Sets and Systems, 399, 20–43.
Eslamipoor, R., Hosseini-Nasab, H., & Sepehriar, A. (2015). An improved ranking method for generalized fuzzy numbers based on Euclidian distance concept. Afrika Matematika. 26(7),1291-1297.
Grewal, B. S. (2017). Higher Engineering Mathematics. New Delhi: Khanna Publishers
Jain, R. (1976). Decisionmaking in the Presence of Fuzzy Variables. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(10), 698–703.
Jiang, W. G., Luo, Y., Qin, X., & Zhan, J. (2015). An improved method to rank generalized fuzzy numbers with different left heights and right heights. Journal of Intelligent and Fuzzy Systems, 28(5),2343–2355.
Murakami, S., Maeda, H., & Imamura, S. (1983). Fuzzy Decision Analysis on the Development of Centralized Regional Energy Control System. IFAC Proceedings Volumes, 16(13), 363–368.
Nejad, A. F., & Mashinchi, M. (2011). Ranking fuzzy numbers based on the areas on the left and the right sides of fuzzy number. Computers & Mathematics with Applications, 61(2), 431–442.
Patra, K. C. (2021). Fuzzy risk analysis uses a new ranking technique of generalized trapezoidal fuzzy numbers. Granular Computing, 7(1), 127–140.
Van Hop, N. (2021). Ranking fuzzy numbers based on relative positions and shape characteristics. Expert Systems With Applications, 191, 116312.
Wang, X., & Kerre, E. E. (2001). Reasonable properties for the ordering of fuzzy quantities (II). Fuzzy sets and systems, 118(3), 387-405.
Wang, Y., & Lee, H. (2008). The revised method ranks fuzzy numbers with an area between the centroid and original points. Computers & Mathematics with Applications, 55(9), 2033–2042.
Wang, Z., Liu, Y., Fan, Z., & Feng, B. (2009). Ranking L–R fuzzy number based on deviation degree. Information Sciences, 179(13), 2070–2077.
Yager, R. R. (1978). Ranking fuzzy subsets over the unit interval. In Proceedings of 17th IEEE international Conference on Decision and Control,1435-1437.
Yao, J., & Wu, K. (2000). Ranking fuzzy numbers based on decomposition principle and signed distance. Fuzzy Sets and Systems, 116(2), 275–288.
Yong, D., & Qi, L. (2005). A TOPSIS-based centroid–index ranking method of fuzzy numbers and its application in decision-making. Cybernetics and Systems, 36(6), 581–595.
Yu, V. F., Chi, H. T. X., & Shen, C. (2013). Ranking fuzzy numbers based on epsilon-deviation degree. Applied Soft Computing, 13(8), 3621–3627.
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
Zimmermann, H. (2013). Fuzzy Set Theory—and Its Applications. Springer Science & Business Media.
Abbasbandy, S., & Hajjari, T. (2009). A new approach for ranking of trapezoidal fuzzy numbers. Computers & Mathematics with Applications, 57(3), 413–419.
Asady, B. (2011). Revision of distance minimization method for ranking of fuzzy numbers. Applied Mathematical Modelling, 35(3), 1306–1313.
Asady, B., & Zendehnam, A. (2007). Ranking fuzzy numbers by distance minimization. Applied Mathematical Modelling, 31(11), 2589–2598.
Chen, S., & Chen, J. (2009). Fuzzy risk analysis is based on ranking generalized fuzzy numbers with different heights and spreads. Expert Systems with Applications, 36(3), 6833–6842.
Chen, S., & Chen, S. (2007). Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Applied Intelligence, 26(1), 1–11.
Chen, S., Munif, A., Chen, G., Liu, H., & Kuo, B. (2012). Fuzzy risk analysis is based on ranking generalized fuzzy numbers with different left and right heights. Expert Systems with Applications, 39(7), 6320–6334.
Chen, S., & Sanguansat, K. (2011). Analyzing fuzzy risk based on a new fuzzy ranking method between generalized fuzzy numbers. Expert Systems with Applications, 38(3), 2163–2171.
Cheng, C. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems, 95(3), 307–317.
Chu, T., & Tsao, C. (2002). Ranking fuzzy numbers with an area between the centroid point and original point. Computers & Mathematics with Applications, 43(1–2), 111–117.
Chutia, R. (2017). Ranking of fuzzy numbers by using value and angle in the epsilon-deviation degree method. Applied Soft Computing, 60, 706–721.
De Hierro, A. F. R. L., Roldán, C., & Herrera, F. (2018). On a new methodology for ranking fuzzy numbers and its application to real economic data. Fuzzy Sets and Systems, 353, 86–110.
Dombi, J., & Jónás, T. (2020). Ranking trapezoidal fuzzy numbers using a parametric relation pair. Fuzzy Sets and Systems, 399, 20–43.
Eslamipoor, R., Hosseini-Nasab, H., & Sepehriar, A. (2015). An improved ranking method for generalized fuzzy numbers based on Euclidian distance concept. Afrika Matematika. 26(7),1291-1297.
Grewal, B. S. (2017). Higher Engineering Mathematics. New Delhi: Khanna Publishers
Jain, R. (1976). Decisionmaking in the Presence of Fuzzy Variables. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(10), 698–703.
Jiang, W. G., Luo, Y., Qin, X., & Zhan, J. (2015). An improved method to rank generalized fuzzy numbers with different left heights and right heights. Journal of Intelligent and Fuzzy Systems, 28(5),2343–2355.
Murakami, S., Maeda, H., & Imamura, S. (1983). Fuzzy Decision Analysis on the Development of Centralized Regional Energy Control System. IFAC Proceedings Volumes, 16(13), 363–368.
Nejad, A. F., & Mashinchi, M. (2011). Ranking fuzzy numbers based on the areas on the left and the right sides of fuzzy number. Computers & Mathematics with Applications, 61(2), 431–442.
Patra, K. C. (2021). Fuzzy risk analysis uses a new ranking technique of generalized trapezoidal fuzzy numbers. Granular Computing, 7(1), 127–140.
Van Hop, N. (2021). Ranking fuzzy numbers based on relative positions and shape characteristics. Expert Systems With Applications, 191, 116312.
Wang, X., & Kerre, E. E. (2001). Reasonable properties for the ordering of fuzzy quantities (II). Fuzzy sets and systems, 118(3), 387-405.
Wang, Y., & Lee, H. (2008). The revised method ranks fuzzy numbers with an area between the centroid and original points. Computers & Mathematics with Applications, 55(9), 2033–2042.
Wang, Z., Liu, Y., Fan, Z., & Feng, B. (2009). Ranking L–R fuzzy number based on deviation degree. Information Sciences, 179(13), 2070–2077.
Yager, R. R. (1978). Ranking fuzzy subsets over the unit interval. In Proceedings of 17th IEEE international Conference on Decision and Control,1435-1437.
Yao, J., & Wu, K. (2000). Ranking fuzzy numbers based on decomposition principle and signed distance. Fuzzy Sets and Systems, 116(2), 275–288.
Yong, D., & Qi, L. (2005). A TOPSIS-based centroid–index ranking method of fuzzy numbers and its application in decision-making. Cybernetics and Systems, 36(6), 581–595.
Yu, V. F., Chi, H. T. X., & Shen, C. (2013). Ranking fuzzy numbers based on epsilon-deviation degree. Applied Soft Computing, 13(8), 3621–3627.
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
Zimmermann, H. (2013). Fuzzy Set Theory—and Its Applications. Springer Science & Business Media.