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A theoretical approach to ranking of parametric fuzzy numbers using value and left–right ambiguity

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Abstract

In this paper, a new method of ranking fuzzy numbers has been proposed. The method is being developed using the ill-defined quantity value as well as the quantities, left ambiguity and right ambiguity of a fuzzy number. It is seen that there are several methods on ranking fuzzy numbers, but in most of the studies, the ranking methods fail to rank some of the fuzzy numbers. Hence, in this paper, a new ranking method has been introduced to overcome these limitations. The ranking index is formulated by using ill-defined quantity value and by the convex combination of left ambiguity and right ambiguity of a fuzzy number with index of optimism. A quantity \(\theta\) is also introduced, which decides the inclusion and exclusion of the ambiguity in the ranking index. Further, the rationality validation of the proposed ranking method is also checked, by proving the Wang and Kerre’s reasonable properties. Furthermore, a comparative study is being performed to show the outperformance of the proposed method.

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References

  1. Abbasbandy, S., Hajjari, T.: A new approach for ranking of trapezoidal fuzzy numbers. Comput. Math. Appl. 57(3), 413–419 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  2. Abbaszadeh Sori, A., Ebrahimnejad, A., Motameni, H.: Elite artificial bees’ colony algorithm to solve robot’s fuzzy constrained routing problem. Comput. Intell. 36(2), 659–681 (2020)

    Article  MathSciNet  Google Scholar 

  3. Abbaszade Sori, A., Ebrahimnejad, A., Motameni, H.: The fuzzy inference approach to solve multi-objective constrained shortest path problem. J. Intell. Fuzzy Syst. 38(4), 4711–4720 (2020)

    Article  Google Scholar 

  4. Allahviranloo, T., Abbasbandy, S., Saneifard, R.: A method for ranking of fuzzy numbers using new weighted distance. Math. Comput. Appl. 16, 359–369 (2011)

    MathSciNet  MATH  Google Scholar 

  5. Allahviranloo, T., Saneifard, R.: Defuzzification method for ranking fuzzy numbers based on center of gravity. Iran. J. Fuzzy Syst. 9, 57–67 (2012)

    MATH  Google Scholar 

  6. Arya, A., Yadav, S.P.: A new approach to rank the decision making units in presence of infeasibility in intuitionistic fuzzy environment. Iran. J. Fuzzy Syst. 17(2), 183–199 (2020)

    MathSciNet  MATH  Google Scholar 

  7. Asady, B., Zendehnam, A.: Ranking fuzzy numbers by distance minimization. Appl. Math. Model. 31(11), 2589–2598 (2007)

    Article  MATH  Google Scholar 

  8. Bortolan, G., Degani, R.: A review of some methods for ranking fuzzy subsets. Fuzzy Sets Syst. 15(1), 1–19 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  9. Brunelli, M., Mezei, J.: How different are ranking methods for fuzzy numbers? A numerical study. Int. J. Approx. Reason. 54(5), 627–639 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chen, C.-C., Tang, H.-C.: Ranking nonnormal p-norm trapezoidal fuzzy numbers with integral value. Comput. Math. Appl. 56(9), 2340–2346 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  11. Chen, S.M.: New methods for subjective mental workload assessment and fuzzy risk analysis. Cybern. Syst. 27(5), 449–472 (1996)

    Article  MATH  Google Scholar 

  12. Chen, S.-M., Chen, J.-H.: Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads. Expert Syst. Appl. 36(3), 6833–6842 (2009)

    Article  Google Scholar 

  13. Chen, S.-M., Sanguansat, K.: Analyzing fuzzy risk based on a new fuzzy ranking method between generalized fuzzy numbers. Expert Syst. Appl. 38(3), 2163–2171 (2011)

    Article  Google Scholar 

  14. Chen, Z., Huang, G., Chakma, A.: Hybrid fuzzy-stochastic modeling approach for assessing environmental risks at contaminated groundwater systems. J. Environ. Eng. 129(1), 79–88 (2003)

    Article  Google Scholar 

  15. Cheng, C.-H.: A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets Syst. 95(3), 307–317 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  16. Chi, H.T.X., Yu, V.F.: Ranking generalized fuzzy numbers based on centroid and rank index. Appl. Soft Comput. 68, 283–292 (2018)

    Article  Google Scholar 

  17. Choobineh, F., Li, H.: An index for ordering fuzzy numbers. Fuzzy Sets Syst. 54(3), 287–294 (1993)

    Article  MathSciNet  Google Scholar 

  18. Chou, S.-Y., Dat, L.Q., Yu, V.F.: A revised method for ranking fuzzy numbers using maximizing set and minimizing set. Comput. Ind. Eng. 61(4), 1342–1348 (2011)

    Article  Google Scholar 

  19. Chu, T.-C., Tsao, C.-T.: Ranking fuzzy numbers with an area between the centroid point and original point. Comput. Math. Appl. 43(1–2), 111–117 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  20. Chutia, R.: Ranking of fuzzy numbers by using value and angle in the epsilon-deviation degree method. Appl. Soft Comput. 60, 706–721 (2017)

    Article  Google Scholar 

  21. Chutia, R.: Ranking of Z-numbers based on value and ambiguity at levels of decision making. Int. J. Intell. Syst. 36(1), 313–331 (2021)

    Article  Google Scholar 

  22. Chutia, R., Chutia, B.: A new method of ranking parametric form of fuzzy numbers using value and ambiguity. Appl. Soft Comput. 52, 1154–1168 (2017)

    Article  Google Scholar 

  23. Chutia, R., Gogoi, M.K., Firozja, M.A., Smarandache, F.: Ordering single-valued neutrosophic numbers based on flexibility parameters and its reasonable properties. Int. J. Intell. Syst. 36(4), 1831–1850 (2021)

    Article  Google Scholar 

  24. Chutia, R., Gogoi, R., Datta, D.: Ranking p-norm generalised fuzzy numbers with different left height and right height using integral values. Math. Sci. 9(1), 1–9 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  25. Chutia, R., Saikia, S.: Ranking intuitionistic fuzzy numbers at levels of decision-making and its application. Expert Syst. 35(5), e12292 (2018)

    Article  Google Scholar 

  26. Chutia, R., Saikia, S.: Ranking of interval type-2 fuzzy numbers using value and ambiguity. In: 2020 International Conference on Computational Performance Evaluation (ComPE), pp. 305–310 (2020)

  27. Darehmiraki, M.: A novel parametric ranking method for intuitionistic fuzzy numbers. Iran. J. Fuzzy Syst. 16(1), 129–143 (2019)

    MathSciNet  MATH  Google Scholar 

  28. Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press Inc., Orlando (1980)

    MATH  Google Scholar 

  29. Ebrahimnejad, A.: An improved approach for solving fuzzy transportation problem with triangular fuzzy numbers. J. Intell. Fuzzy Syst. 29, 963–974 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  30. Ebrahimnejad, A.: Fuzzy linear programming approach for solving transportation problems with interval-valued trapezoidal fuzzy numbers. Sadhana 41, 299–316 (2016)

    MathSciNet  MATH  Google Scholar 

  31. Ebrahimnejad, A.: New method for solving fuzzy transportation problems with LR flat fuzzy numbers. Inf. Sci. 357, 108–124 (2016)

    Article  MATH  Google Scholar 

  32. Ebrahimnejad, A., Verdegay, J.L.: An efficient computational approach for solving type-2 intuitionistic fuzzy numbers based transportation problems. Int. J. Comput. Intell. Syst. 9(6), 1154–1173 (2016)

    Article  Google Scholar 

  33. Ezzati, R., Allahviranloo, T., Khezerloo, S., Khezerloo, M.: An approach for ranking of fuzzy numbers. Expert Syst. Appl. 39, 690–695 (2012)

    Article  MATH  Google Scholar 

  34. Ezzati, R., Enayati, R., Mottaghi, A., Saneifard, R.: A new method for ranking fuzzy numbers without concerning of real numbers. TWMS J. Pure Appl. Math. 2, 256–270 (2011)

    MathSciNet  MATH  Google Scholar 

  35. Ezzati, R., Khezerloo, S., Ziari, S.: Application of parametric form for ranking of fuzzy numbers. Iran. J. Fuzzy Syst. 12, 59–74 (2015)

    MathSciNet  MATH  Google Scholar 

  36. Ezzati, R., Saneifard, R.: A new approach for ranking of fuzzy numbers with continuous weighted quasi-arithmetic means. Math. Sci. 4, 143–158 (2010)

    MATH  Google Scholar 

  37. Hajjari, T., Abbasbandy, S.: A note on “the revised method of ranking L-R fuzzy number based on deviation degree.” Expert Syst. Appl. 38(10), 13491–13492 (2011)

  38. Jain, R.: Decision-making in the presence of fuzzy variables. IEEE Trans. Syst. Man Cybern. SMC—-6(10), 698–703 (1976)

    MATH  Google Scholar 

  39. Jain, R.: A procedure for multiple-aspect decision making using fuzzy sets. Int. J. Syst. Sci. 8(1), 1–7 (1977)

    Article  MATH  Google Scholar 

  40. Kim, K., Park, K.S.: Ranking fuzzy numbers with index of optimism. Fuzzy Sets Syst. 35(2), 143–150 (1990)

    Article  MathSciNet  Google Scholar 

  41. Kumar, A., Singh, P., Kaur, A., Kaur, P.: A new approach for ranking nonnormal p-norm trapezoidal fuzzy numbers. Comput. Math. Appl. 61(4), 881–887 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  42. Liou, T.-S., Wang, M.-J.J.: Ranking fuzzy numbers with integral value. Fuzzy Sets Syst. 50(3), 247–255 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  43. Liu, P., Chen, S.-M., Wang, Y.: Multi attribute group decision making based on intuitionistic fuzzy partitioned Maclaurin symmetric mean operators. Inf. Sci. 512, 830–854 (2020)

    Article  MATH  Google Scholar 

  44. Liu, P., Wang, P.: Multiple attribute group decision making method based on intuitionistic fuzzy Einstein interactive operations. Int. J. Fuzzy Syst. 22, 790–809 (2020)

    Article  Google Scholar 

  45. Nasseri, S., Zadeh, M., Kardoost, M., Behmanesh, E.: Ranking fuzzy quantities based on the angle of the reference functions. Appl. Math. Model. 37(22), 9230–9241 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  46. Nayagam, V.L.G., Jeevaraj, S., Dhanasekaran, P.: An improved ranking method for comparing trapezoidal intuitionistic fuzzy numbers and its applications to multicriteria decision making. Neural Comput. Appl. 30(2), 671–682 (2018)

    Article  Google Scholar 

  47. Rezvani, S.: Ranking generalized exponential trapezoidal fuzzy numbers based on variance. Appl. Math. Comput. 262, 191–198 (2015)

    MathSciNet  MATH  Google Scholar 

  48. Shakouri, B., Abbasi Shureshjani, R., Daneshian, B., Lotfi, F.: A parametric method for ranking intuitionistic fuzzy numbers and its application to solve intuitionistic fuzzy network data envelopment analysis models. Complexity 2020, 6408613 (2020)

    Article  MATH  Google Scholar 

  49. Shureshjani, R.A., Darehmiraki, M.: A new parametric method for ranking fuzzy numbers. Indag. Math. 24(3), 518–529 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  50. Wang, X., Kerre, E.E.: Reasonable properties for the ordering of fuzzy quantities (I). Fuzzy Sets Syst. 118(3), 375–385 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  51. Wang, X., Kerre, E.E.: Reasonable properties for the ordering of fuzzy quantities (II). Fuzzy Sets Syst. 118(3), 387–405 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  52. Wang, Y.-J., Lee, H.-S.: The revised method of ranking fuzzy numbers with an area between the centroid and original points. Comput. Math. Appl. 55(9), 2033–2042 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  53. Wang, Y.-M., Yang, J.-B., Xu, D.-L., Chin, K.-S.: On the centroids of fuzzy numbers. Fuzzy Sets Syst. 157(7), 919–926 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  54. Yager, R.: Ranking fuzzy subsets over the unit interval. In: IEEE Conference on decision and control including the 17th symposium on adaptive processes, pp. 1435–1437 (1978)

  55. Yu, V.F., Dat, L.Q.: An improved ranking method for fuzzy numbers with integral values. Appl. Soft Comput. 14 Part C, 603–608 (2014)

    Article  Google Scholar 

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Correspondence to Rituparna Chutia.

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Chutia, R., Saikia, S. & Gogoi, M.K. A theoretical approach to ranking of parametric fuzzy numbers using value and left–right ambiguity. Math Sci 16, 299–315 (2022). https://doi.org/10.1007/s40096-021-00422-4

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