Triangular Intuitionistic Fuzzy Triple Bonferroni Harmonic Mean Operators and Application to Multi-attribute Group Decision Making

Document Type : Research Paper

Authors

College of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China

Abstract

As an special intuitionistic fuzzy set defined on the real number set, triangular intuitionistic fuzzy number (TIFN) is a fundamental tool for quantifying an ill-known quantity. In order to model the decision maker's overall preference with mandatory requirements, it is necessary to develop some Bonferroni harmonic mean operators for TIFNs which can be used to effectively intergrate the information of attribute values for multi-attribute group decision making (MAGDM) with TIFNs. The purpose of this paper is to develop some Bonferroni harmonic operators of TIFNs and apply to the MAGDM problems with TIFNs. The weighted possibility means of TIFN are firstly defined. Hereby, a new lexicographic approach is presented to rank TIFNs sufficiently considering the risk preference of decision maker. The sensitivity analysis on the risk preference parameter is made. Then, three kinds of triangular intuitionistic fuzzy Bonferroni harmonic aggregation operators are defined, including a triangular intuitionistic fuzzy triple weighted Bonferroni harmonic mean operator (TIFTWBHM) operator, a triangular intuitionistic fuzzy triple ordered weighted Bonferroni harmonic mean (TIFTOWBHM) operator and a triangular intuitionistic fuzzy triple hybrid Bonferroni harmonic mean (TIFTHBHM) operator. Some desirable properties for these operators are discussed in detail. By using the TIFTWBHM operator, we can obtain the individual overall attribute values of alternatives, which are further integrated into the collective ones by the TIFTHBHM operator. The ranking order of alternatives is generated according to the collective overall attribute values of alternatives. A real investment selection case study verifies the validity and applicability of the proposed method.

Keywords


[1] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1) (1986), 87{96.
[2] G. Beliakov, S. James, J. Mordelova, T. Ruckschlossova and R. Yager, Generalized Bon-
ferroni mean operators in multi-criteria aggregation, Fuzzy Sets and Systems, 161 (2010),
2227{2242.
[3] C. Bonferroni, Sulle medie multiple di potenze, Bolletino Matematica Italiana, 5 (1950),
267{270.
[4] C. Carlsson and R. Fuller, On possibilistic mean value and deviation of fuzzy numbers,
Fuzzy Sets and Systems, 122 (2001), 315-326.
[5] H. Y. Chen, C. L. Liu and Z. H. Sheng, Induced ordered weighted harmonic averaging
(IOWHA) operator and its application to combination forecasting method, Chinese Journal
of Management Science, 12(5) (2004), 35-40.
[6] J. Y. Dong and S. P. Wan, A new method for multi-attribute group decision making with
triangular intuitionistic fuzzy numbers, Kybernetes, 45(1) (2016), 158-180.
[7] J. Y. Dong and S. P.Wan, A new method for prioritized multi-criteria group decision mak-
ing with triangular intuitionistic fuzzy numbers, Journal of intelligent and Fuzzy systems,
30 (2016), 1719-1733.
[8] B. Dutta and D. Guha, Trapezoidal intuitionistic fuzzy Bonferroni means and its applia-
tion in multi-attribute decision making, Fuzzy Systems (FUZZ), (2013) IEEE International
Conference on. IEEE, (2013), 1-8.
[9] D. P. Filev and R. R. Yager, On the issue of obtaining OWA operator weights, Fuzzy Sets
and Systems, 94 (2) (1998), 157-169.
[10] R. Fuller and P. Majlender, On weighted possibilistic mean and variance of fuzzy numbers,
Fuzzy Sets and Systems, 136 (2003), 363-374.
[11] D. F. Li, A note on "using intuitionistic fuzzy sets for fault-tree analysis on printed circuit
board assembly", Microelectronics Reliability, 48(10) (2008), 1741.
[12] D. F. Li, A ratio ranking method of triangular intuitionistic fuzzy numbers and its appli-
cation to MADM problems, Computers and Mathematics with Applications, 60 (2010),
1557-1570.
[13] D. F. Li, J. X. Nan and M. J. Zhang, A ranking method of triangular intuitionistic fuzzy
numbers and application to decision making, International Journal of Computational Intelligence
Systems, 3(5) (2010), 522-530.
[14] J. X. Nan, D. F. Li and M. J. Zhang, A lexicographic method for matrix games with
payo s of triangular intuitionistic fuzzy numbers, International Journal of Computational
Intelligence Systems, 3(3) (2010), 280-289.
[15] J. H. Park and E. J. Park, Generalized fuzzy Bonferroni harmonic mean operators
and their applications in group decision making, Journal of Applied Mathematics(2013),
http://dx.doi.org/10.1155/2013/604029.
[16] M. H. Shu, C. H. Cheng and J. R. Chang, Using intuitionistic fuzzy sets for fault tree
analysis on printed circuit board assembly, Microelectronics Reliability, 46(12) (2006),
2139-2148.
[17] H. Sun and M. Sun, Generalized Bonferroni harmonic mean operators and their applica-
tion to multiple attribute decision making, Journal of Computational Information Systems,
8 (2012), 5717-5724.
[18] S. P. Wan, G. L. Xu, F. Wang and J. Y. Dong, A new method for Atanassov's interval-
valued intuitionistic fuzzy MAGDM with incomplete attribute weight information, Information
Sciences, 316 (2015), 329-347.
[19] S. P. Wan and D. F. Li, Fuzzy mathematical programming approach to heterogeneous
multiattribute decision-making with interval-valued intuitionistic fuzzy truth degrees, Information
Sciences, 325 (2015), 484-503.
[20] S. P. Wan and J. Y. Dong, Interval-valued intuitionistic fuzzy mathematical programming
method for hybrid multi-criteria group decision making with interval-valued intuitionistic
fuzzy truth degrees, Information Fusion, 26 (2015), 49-65.
[21] S. P. Wan and J. Y. Dong, Power geometric operators of trapezoidal intuitionistic fuzzy
numbers and application to multi-attribute group decision making, Applied Soft Computing,
29 (2015), 153-168.
[22] S. P. Wan, F. Wang and J. Y. Dong,A novel group decision making method with intuition-
istic fuzzy preference relations for RFID technology selection, Applied Soft Computing,
38 (2016), 405-422.
[23] S. P. Wan, F. Wang, L. L. Lin and J. Y. Dong,An intuitionistic fuzzy linear programming
method for logistics outsourcing provider selection, Knowledge-Based Systems, 82 (2015),
80-94.
[24] S. P. Wan, F. Wang and J. Y. Dong,A novel risk attitudinal ranking method for intu-
itionistic fuzzy values and application to MADM, Applied Soft Computing, 40 (2016),
98-112.
[25] S. P. Wan, Multi-attribute decision making method based on possibility variance coecient
of triangular intuitionistic fuzzy numbers, International Journal of Uncertainty, Fuzziness
and Knowledge-Based Systems, 21(2) (2013), 223-243.
[26] S. P. Wan, Q. Y. Wang and J. Y. Dong, The extended VIKOR method for multi-attribute
group decision making with triangular intuitionistic fuzzy numbers, Knowledge-Based Systems,
52 (2013), 65-77.
[27] S. P. Wan and J. Y. Dong, Possibility method for triangular intuitionistic fuzzy multi-
attribute group decision making with incomplete weight information, International Journal
of Computational Intelligence Systems, 7(1) (2014), 65-79.
[28] S. P. Wan, F. Wang and L. L. Lin, Some new generalized aggregation operators for tri-
angular intuitionistic fuzzy numbers and application to multi-attribution group decision
making, Computers and Industrial Engineering, 93 (2016), 286-301.
[29] S. P. Wan, L. L. Lin and J. Y. Dong, MAGDM based on triangular Atanassov's intuition-
istic fuzzy information aggregation, Neural Computing and Applications, 27(2) (2016),
http://dx.doi.org/ 10.1007/s00521-016-2196-9.
[30] J. Q.Wang, R. R. Nie, H. Y. Zhang and X. H. Chen, New operators on triangular intuition-
istic fuzzy numbers and their applications in system fault analysis, Information Sciences,
251 (2013), 79-95.
[31] G. W. Wei, FIOWHM operator and its application to multiple attribute group decision
making, Expert Systems with Applications, 38 (2011), 2984-2989.
[32] M. M. Xia, Z. S. Xu and B. Zhu, Generalized intuitionistic fuzzy Bonferroni means, International
Journal of Intelligent Systems, 27(1) (2012), 23-47.
[33] Z. S. Xu, An overview of methods for determining OWA weights, International Journal of
Intelligent Systems, 20(8) (2005), 843-865.
[34] Z. S. Xu and R. R. Yager, Intuitionistic fuzzy Bonferroni means, IEEE Transactions on
System, Man, and Cybernetics-part B, 46 (2010), 568-578.
[35] Z. S. Xu, Fuzzy harmonic mean operator, International Journal of Intelligent Systems, 24
(2009), 152-172.
[36] R. R. Yager, Prioritized OWA aggregation, Fuzzy Optimization and Decision Making, 8
(2009), 245-262.
[37] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338-356.