How to cite this paper
Peddi, P. (2019). Defuzzification method for ranking fuzzy numbers based on centroids and maximizing and minimizing set.Decision Science Letters , 8(4), 411-428.
Refrences
Abbasbandy, S., &Asady, B. (2006). Ranking of fuzzy numbers by sign distance. Information Sciences, 176(16), 2405-2416.
Chen, S. H. (1985). Ranking fuzzy numbers with maximizing set and minimizing set. Fuzzy sets and Systems, 17(2), 113-129.
Chen, S. M., & Chen, J. H. (2009). Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads. Expert systems with applications, 36(3), 6833-6842.
Chen, S. J., & Chen, S. M. (2007). Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Applied intelligence, 26(1), 1-11.
Cheng, C. H. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy sets and systems, 95(3), 307-317.
Chu, T. C., &Tsao, C. T. (2002). Ranking fuzzy numbers with an area between the centroid point and original point. Computers & Mathematics with Applications, 43(1-2), 111-117.
Chou, S. Y., Dat, L. Q., & Vincent, F. Y. (2011). A revised method for ranking fuzzy numbers using maximizing set and minimizing set. Computers & Industrial Engineering, 61(4), 1342-1348.
Dubois, D., &Prade, H. (1978). Operations on fuzzy numbers. International Journal of systems science, 9(6), 613-626.
Fortemps, P., &Roubens, M. (1996). Ranking and defuzzification methods based on area compensation. Fuzzy Sets and Systems, 82(3), 319-330.
Jain, R. (1978). Decision-making in the presence of a fuzzy variable. IEEE Transactions on Systems, Man and Cybernetics, 6, 698-703.
Lee, L. W., & Chen, S. M. (2008). Fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations. Expert Systems with Applications, 34(4), 2763-2771.
Wang, Y. M., & Luo, Y. (2009). Area ranking of fuzzy numbers based on positive and negative ideal points. Computers & Mathematics with Applications, 58(9), 1769-1779.
Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information sciences, 24(2), 143-161.
Yao, J. S., & 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: An International Journal, 36(6), 581-595.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Chen, S. H. (1985). Ranking fuzzy numbers with maximizing set and minimizing set. Fuzzy sets and Systems, 17(2), 113-129.
Chen, S. M., & Chen, J. H. (2009). Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads. Expert systems with applications, 36(3), 6833-6842.
Chen, S. J., & Chen, S. M. (2007). Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Applied intelligence, 26(1), 1-11.
Cheng, C. H. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy sets and systems, 95(3), 307-317.
Chu, T. C., &Tsao, C. T. (2002). Ranking fuzzy numbers with an area between the centroid point and original point. Computers & Mathematics with Applications, 43(1-2), 111-117.
Chou, S. Y., Dat, L. Q., & Vincent, F. Y. (2011). A revised method for ranking fuzzy numbers using maximizing set and minimizing set. Computers & Industrial Engineering, 61(4), 1342-1348.
Dubois, D., &Prade, H. (1978). Operations on fuzzy numbers. International Journal of systems science, 9(6), 613-626.
Fortemps, P., &Roubens, M. (1996). Ranking and defuzzification methods based on area compensation. Fuzzy Sets and Systems, 82(3), 319-330.
Jain, R. (1978). Decision-making in the presence of a fuzzy variable. IEEE Transactions on Systems, Man and Cybernetics, 6, 698-703.
Lee, L. W., & Chen, S. M. (2008). Fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations. Expert Systems with Applications, 34(4), 2763-2771.
Wang, Y. M., & Luo, Y. (2009). Area ranking of fuzzy numbers based on positive and negative ideal points. Computers & Mathematics with Applications, 58(9), 1769-1779.
Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information sciences, 24(2), 143-161.
Yao, J. S., & 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: An International Journal, 36(6), 581-595.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.