WSEAS Transactions on Mathematics


Print ISSN: 1109-2769
E-ISSN: 2224-2880

Volume 17, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.


Volume 17, 2018



One Method of Finding an Intergroup Consensus based on Triangular Fuzzy Numbers

AUTHORS: Teimuraz Tsabadze, Archil Prangishvili

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This paper introduces one method of reaching consensus among several groups of experts. The method is based on the use of fuzzy numbers. It is meant that opinions of experts are expressed by fuzzy triangular numbers and, therefore, several finite collections of fuzzy triangular numbers are obtained. A method for aggregation of the obtained several finite collections of fuzzy sets into the resulting one is proposed. A new approach is introduced for determining degrees of experts’ importance depending on the closeness of experts’ estimates to the representative of a finite collection of all triangular fuzzy estimates. The specific fuzzy aggregation operator is offered. The proposed method is thoroughly discussed and its algorithm is presented.

KEYWORDS: Consensus, Finite collection of fuzzy triangular numbers, Metric lattice, Regulation, Representative, Fuzzy aggregation operator, Algorithm

REFERENCES:

[1] D. Dubois, H. Prade, Systems on linear fuzzy constraints, Fuzzy Sets and Systems, Vol.3, No.1, 1980, pp. 37-48.

[2] Debashree Guha, Debjani Chakraborty, A new approach to fuzzy distance measure and similarity measure between two generalized fuzzy numbers, Applied Soft Computing, Vol.10, Issue 1, 2010, pp. 90-99

[3] A. Kauffman and M.M. Gupta, Introduction to Fuzzy Arithmetic: Theory and Applications

[Van Nostrand Reinhold, New York, 1985].

[4] T. Tsabadze, A method for fuzzy aggregation based on grouped expert evaluations, Fuzzy Sets and Systems 157 (2006) pp. 1346-1361.

[5] T. Tsabadze, An Approach for Aggregation of Experts’ Qualitative Evaluations by Means of Fuzzy Sets. 2013 IFSA World Congress NAFIPS Annual Meeting, Edmonton, Canada, 2013.

[6] T. Tsabadze, A method for aggregation of trapezoidal fuzzy estimates under group decision-making, Int. J. Fuzzy Sets and Systems, 266(2015), pp. 114-130. {7] J. Vaníček, I. Vrana and S. Aly, Fuzzy aggregation and averaging for group decision making: A generalization and survey. Knowledge-Based Systems 22 (2009) pp. 79-84.

[8] L. A. Zadeh, The concept of linguistic variable and its application to approximate reasoning – Part 1, Information Sciences, 8, 1975 , pp. 199- 249.

WSEAS Transactions on Mathematics, ISSN / E-ISSN: 1109-2769 / 2224-2880, Volume 17, 2018, Art. #48, pp. 398-403


Copyright Β© 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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