Main Article Content
Abstract
Even though a large number of research studies have been presented in recent years for ranking and comparing fuzzy numbers, the majority of existing techniques suffer from plenty of shortcomings. These shortcomings include counterintuitiveness, the inability to distinguish the fuzzy number and its partnered image, and the inconsistent ability to distinguish symmetric fuzzy numbers and fuzzy numbers that represent the compensation of areas. To overcome the cited drawbacks, this paper suggests a unified distance that multiplies the centroid value (weighted mean value) of the fuzzy number on the horizontal axis and a linear sum of the
distances of the centroid points of the left and right fuzziness areas from the original
point through an indicator. The indicator reflects the attitude of the left and
right fuzziness of the fuzzy number, we can call it the indicator of fuzziness. To use
this technique, the membership functions of the fuzzy numbers need not be linear.
That is the proposed approach can also rank the fuzzy numbers with non-linear
membership functions. The suggested technique is highly convenient and reliable to
discriminate the symmetric fuzzy numbers and the fuzzy numbers having compensation
of areas. The advantages of the proposed approach are illustrated through
examples that are common for a wide range of numerical studies and comparisons
with several representative approaches, that existed in the literature.
Keywords
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
- Abbasbandy, S. and Asady, B. “Ranking of fuzzy numbers by sign distance.” Information
- Sciences, 176(16), (2006), 2405–2416.
- Abbasbandy, S. and Hajjari, T. “A new approach for ranking of trapezoidal fuzzy numbers.”
- Computers Mathematics with Applications, 57(3), (2009), 413–419.
- Abbasi Shureshjani, R. and Darehmiraki, M. “A new parametric method for ranking fuzzy
- numbers.” Indagationes Mathematicae, 24(3), (2013), 518–529.
- Asady, B. “Revision of distance minimization method for ranking of fuzzy numbers.” Applied
- Mathematical Modelling, 35(3), (2011), 1306–1313.
- Asady, B. and Zendehnam, A. “Ranking fuzzy numbers by distance minimization.” Applied
- Mathematical Modelling, 31(11), (2007), 2589–2598.
- Barazandeh Hayati, Y. and Ghazanfari, B. “A novel method for ranking generalized fuzzy
- numbers with two different heights and its application in fuzzy risk analysis.” Iranian Journal
- of Fuzzy Systems, 18(2), (2021), 81–91.
- Bortolan, G. and Degani, R. “A review of some methods for ranking fuzzy subsets.” Fuzzy
- Sets and Systems, 15(1), (1985), 1–19.
- Chen, S.-H. “Ranking fuzzy numbers with maximizing set and minimizing set.” Fuzzy Sets
- and Systems, 17(2), (1985), 113–129.
- Cheng, C.-H. “A new approach for ranking fuzzy numbers by distance method.” Fuzzy Sets
- and Systems, 95(3), (1998), 307–317.
- Chi, H. T. X. and Yu, V. F. “Ranking generalized fuzzy numbers based on centroid and rank
- index.” Applied Soft Computing, 68, (2018), 283–292
- Choobineh, F. and Li, H. “An index for ordering fuzzy numbers.” Fuzzy Sets and Systems,
- (3), (1993), 287–294.
- Chu, T.-C. and Tsao, C.-T. “Ranking fuzzy numbers with an area between the centroid point
- and original point.” Computers Mathematics with Applications, 43(1), (2002), 111–117.
- Chutia, R. “Ranking of fuzzy numbers by using value and angle in the epsilon-deviation
- degree method.” Applied Soft Computing, 60, (2017), 706–721.
- Chutia, R. and Chutia, B. “A new method of ranking parametric form of fuzzy numbers
- using value and ambiguity.” Applied Soft Computing, 52, (2017), 1154–1168.
- Deng, Y., Zhenfu, Z. and Qi, L. “Ranking fuzzy numbers with an area method using radius
- of gyration.” Computers Mathematics with Applications, 51(6), (2006), 1127–1136.
- Dombi, J. and J´on´as, T. “Ranking trapezoidal fuzzy numbers using a parametric relation
- pair.” Fuzzy Sets and Systems, 399, (2020), 20–43. Fuzzy Intervals.
- Dubois, D. and Prade, H. “Operations on fuzzy numbers.” International Journal of Systems
- Science, 9(6), (1978), 613–626.
- Fortemps, P. and Roubens, M. “Ranking and defuzzification methods based on area compensation.” Fuzzy Sets and Systems, 82(3), (1996), 319–330.
- Hajjari, T. “Ranking fuzzy numbers by similarity measure index.” ICACS’20. Association for
- Computing Machinery, New York, NY, USA (2020). 12–16.
- Jain, R. “Decisionmaking in the presence of fuzzy variables.” IEEE Transactions on Systems,
- Man, and Cybernetics, 6(10), (1976), 698–703.
- Liou, T.-S. and Wang, M.-J. J. “Ranking fuzzy numbers with integral value.” Fuzzy Sets and
- Systems, 50(3), (1992), 247–255.
- Mao, Q. S. “Ranking fuzzy numbers based on weighted distance.” Journal of Physics: Conference Series, 1176, (2019), 032007.
- Nasseri, S., Zadeh, M., Kardoost, M. and Behmanesh, E. “Ranking fuzzy quantities based
- on the angle of the reference functions.” Applied Mathematical Modelling, 37(22), (2013),
- –9241.
- Nasseri, S. H., Taghi-Nezhad, N. and Ebrahimnejad, A. “A note on ranking fuzzy numbers
- with an area method using circumcenter of centroids.” Fuzzy Information and Engineering,
- (2), (2017), 259–268.
- Nguyen, T.-L. “Methods in ranking fuzzy numbers: A unified index and comparative reviews.” Fuzzy Calculus Theory and Its Applications, 1.
- Prasad, S. and Sinha, A. “Ranking fuzzy numbers with mean value of points and comparative
- reviews.” Journal of Scientific Research, 14(3), (2022), 755–771.
- Prasad, S. and Sinha, A. “Ranking fuzzy numbers with unified integral value and comparative
- reviews.” Journal of Scientific Research, 14(1), (2022), 131–151.
- Rao, P. B. “Defuzzification method for ranking fuzzy numbers based on centroids and maximizing and minimizing set.” Decision Science Letters, 8, (2019), 411–428.
- Rao, P. P. B. and Shankar, N. R. “Ranking fuzzy numbers with an area method using
- circumcenter of centroids.” Fuzzy Information and Engineering, 5(1), (2013), 3–18.
- Rezvani, S. “Ranking generalized exponential trapezoidal fuzzy numbers based on variance.”
- Applied Mathematics and Computation, 262, (2015), 191–198.
- Soccoro Garcia, M. and Teresa Lamata, M. “A modification of the index of liou and wang
- for ranking fuzzy number.” International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems, 15(04), (2007), 411–424.
- Wang, Y.-J. and Lee, H.-S. “The revised method of ranking fuzzy numbers with an area
- between the centroid and original points.” Computers Mathematics with Applications, 55(9),
- (2008), 2033–2042.
- Yager, R. R. “Ranking fuzzy subsets over the unit interval.” “1978 IEEE Conference on
- Decision and Control including the 17th Symposium on Adaptive Processes,” (1978). 1435–
- Yager, R. R. “On choosing between fuzzy subsets.” 9(2), (1980), 151–154
- Yager, R. R. “A procedure for ordering fuzzy subsets of the unit interval.” Information
- Sciences, 24(2), (1981), 143–161.
- Yu, V. F., Chi, H. T. X., Dat, L. Q., Phuc, P. N. K. and wen Shen, C. “Ranking generalized
- fuzzy numbers in fuzzy decision making based on the left and right transfer coefficients and
- areas.” Applied Mathematical Modelling, 37(16), (2013), 8106–8117.
- Yu, V. F., Chi, H. T. X. and wen Shen, C. “Ranking fuzzy numbers based on epsilon-deviation
- degree.” Applied Soft Computing, 13(8), (2013), 3621–3627.
- Yua, V. F. and Dat, L. Q. “An improved ranking method for fuzzy numbers with integral
- values.” Applied Soft Computing, 14(1), (2014), 603–608.
- Zadeh, L. A. “Fuzzy sets.” Information and Control, 8(3), (1965), 338–353.
- Zhang, F., Ignatius, J., Lim, C. P. and Zhao, Y. “A new method for ranking fuzzy numbers and its application to group decision making.” Applied Mathematical Modelling, 38(4),
- (2014), 1563–1582
References
Abbasbandy, S. and Asady, B. “Ranking of fuzzy numbers by sign distance.” Information
Sciences, 176(16), (2006), 2405–2416.
Abbasbandy, S. and Hajjari, T. “A new approach for ranking of trapezoidal fuzzy numbers.”
Computers Mathematics with Applications, 57(3), (2009), 413–419.
Abbasi Shureshjani, R. and Darehmiraki, M. “A new parametric method for ranking fuzzy
numbers.” Indagationes Mathematicae, 24(3), (2013), 518–529.
Asady, B. “Revision of distance minimization method for ranking of fuzzy numbers.” Applied
Mathematical Modelling, 35(3), (2011), 1306–1313.
Asady, B. and Zendehnam, A. “Ranking fuzzy numbers by distance minimization.” Applied
Mathematical Modelling, 31(11), (2007), 2589–2598.
Barazandeh Hayati, Y. and Ghazanfari, B. “A novel method for ranking generalized fuzzy
numbers with two different heights and its application in fuzzy risk analysis.” Iranian Journal
of Fuzzy Systems, 18(2), (2021), 81–91.
Bortolan, G. and Degani, R. “A review of some methods for ranking fuzzy subsets.” Fuzzy
Sets and Systems, 15(1), (1985), 1–19.
Chen, S.-H. “Ranking fuzzy numbers with maximizing set and minimizing set.” Fuzzy Sets
and Systems, 17(2), (1985), 113–129.
Cheng, C.-H. “A new approach for ranking fuzzy numbers by distance method.” Fuzzy Sets
and Systems, 95(3), (1998), 307–317.
Chi, H. T. X. and Yu, V. F. “Ranking generalized fuzzy numbers based on centroid and rank
index.” Applied Soft Computing, 68, (2018), 283–292
Choobineh, F. and Li, H. “An index for ordering fuzzy numbers.” Fuzzy Sets and Systems,
(3), (1993), 287–294.
Chu, T.-C. and Tsao, C.-T. “Ranking fuzzy numbers with an area between the centroid point
and original point.” Computers Mathematics with Applications, 43(1), (2002), 111–117.
Chutia, R. “Ranking of fuzzy numbers by using value and angle in the epsilon-deviation
degree method.” Applied Soft Computing, 60, (2017), 706–721.
Chutia, R. and Chutia, B. “A new method of ranking parametric form of fuzzy numbers
using value and ambiguity.” Applied Soft Computing, 52, (2017), 1154–1168.
Deng, Y., Zhenfu, Z. and Qi, L. “Ranking fuzzy numbers with an area method using radius
of gyration.” Computers Mathematics with Applications, 51(6), (2006), 1127–1136.
Dombi, J. and J´on´as, T. “Ranking trapezoidal fuzzy numbers using a parametric relation
pair.” Fuzzy Sets and Systems, 399, (2020), 20–43. Fuzzy Intervals.
Dubois, D. and Prade, H. “Operations on fuzzy numbers.” International Journal of Systems
Science, 9(6), (1978), 613–626.
Fortemps, P. and Roubens, M. “Ranking and defuzzification methods based on area compensation.” Fuzzy Sets and Systems, 82(3), (1996), 319–330.
Hajjari, T. “Ranking fuzzy numbers by similarity measure index.” ICACS’20. Association for
Computing Machinery, New York, NY, USA (2020). 12–16.
Jain, R. “Decisionmaking in the presence of fuzzy variables.” IEEE Transactions on Systems,
Man, and Cybernetics, 6(10), (1976), 698–703.
Liou, T.-S. and Wang, M.-J. J. “Ranking fuzzy numbers with integral value.” Fuzzy Sets and
Systems, 50(3), (1992), 247–255.
Mao, Q. S. “Ranking fuzzy numbers based on weighted distance.” Journal of Physics: Conference Series, 1176, (2019), 032007.
Nasseri, S., Zadeh, M., Kardoost, M. and Behmanesh, E. “Ranking fuzzy quantities based
on the angle of the reference functions.” Applied Mathematical Modelling, 37(22), (2013),
–9241.
Nasseri, S. H., Taghi-Nezhad, N. and Ebrahimnejad, A. “A note on ranking fuzzy numbers
with an area method using circumcenter of centroids.” Fuzzy Information and Engineering,
(2), (2017), 259–268.
Nguyen, T.-L. “Methods in ranking fuzzy numbers: A unified index and comparative reviews.” Fuzzy Calculus Theory and Its Applications, 1.
Prasad, S. and Sinha, A. “Ranking fuzzy numbers with mean value of points and comparative
reviews.” Journal of Scientific Research, 14(3), (2022), 755–771.
Prasad, S. and Sinha, A. “Ranking fuzzy numbers with unified integral value and comparative
reviews.” Journal of Scientific Research, 14(1), (2022), 131–151.
Rao, P. B. “Defuzzification method for ranking fuzzy numbers based on centroids and maximizing and minimizing set.” Decision Science Letters, 8, (2019), 411–428.
Rao, P. P. B. and Shankar, N. R. “Ranking fuzzy numbers with an area method using
circumcenter of centroids.” Fuzzy Information and Engineering, 5(1), (2013), 3–18.
Rezvani, S. “Ranking generalized exponential trapezoidal fuzzy numbers based on variance.”
Applied Mathematics and Computation, 262, (2015), 191–198.
Soccoro Garcia, M. and Teresa Lamata, M. “A modification of the index of liou and wang
for ranking fuzzy number.” International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems, 15(04), (2007), 411–424.
Wang, Y.-J. and Lee, H.-S. “The revised method of ranking fuzzy numbers with an area
between the centroid and original points.” Computers Mathematics with Applications, 55(9),
(2008), 2033–2042.
Yager, R. R. “Ranking fuzzy subsets over the unit interval.” “1978 IEEE Conference on
Decision and Control including the 17th Symposium on Adaptive Processes,” (1978). 1435–
Yager, R. R. “On choosing between fuzzy subsets.” 9(2), (1980), 151–154
Yager, R. R. “A procedure for ordering fuzzy subsets of the unit interval.” Information
Sciences, 24(2), (1981), 143–161.
Yu, V. F., Chi, H. T. X., Dat, L. Q., Phuc, P. N. K. and wen Shen, C. “Ranking generalized
fuzzy numbers in fuzzy decision making based on the left and right transfer coefficients and
areas.” Applied Mathematical Modelling, 37(16), (2013), 8106–8117.
Yu, V. F., Chi, H. T. X. and wen Shen, C. “Ranking fuzzy numbers based on epsilon-deviation
degree.” Applied Soft Computing, 13(8), (2013), 3621–3627.
Yua, V. F. and Dat, L. Q. “An improved ranking method for fuzzy numbers with integral
values.” Applied Soft Computing, 14(1), (2014), 603–608.
Zadeh, L. A. “Fuzzy sets.” Information and Control, 8(3), (1965), 338–353.
Zhang, F., Ignatius, J., Lim, C. P. and Zhao, Y. “A new method for ranking fuzzy numbers and its application to group decision making.” Applied Mathematical Modelling, 38(4),
(2014), 1563–1582