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Information 2015, 6(4), 880-894; doi:10.3390/info6040880

An Interval-Valued Intuitionistic Fuzzy MADM Method Based on a New Similarity Measure

1
School of Software, Jiangxi University of Science and Technology, Nanchang 330013, China
2
School of Mathematics and Statistics, Central South University, Changsha 410000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 26 September 2015 / Revised: 7 December 2015 / Accepted: 8 December 2015 / Published: 18 December 2015
View Full-Text   |   Download PDF [708 KB, uploaded 18 December 2015]

Abstract

Similarity measure is one of the most important measures of interval-valued intuitionistic fuzzy (IVIF) sets. This article will put forward a new similarity measure, which considers the impacts of membership degree, nonmembership degree and median point of IVIF sets. For cases with partially known attribute weight information in multi-attribute decision-making (MADM) problems, a new weighting method is put forward by establishing the maximum similarity optimization model to solve the optimal weights. Further, a new decision-making method is developed on the basis of proposed similarity measure, and an applied example proves the effectiveness and feasibility of the proposed methods. View Full-Text
Keywords: similarity measure; interval-valued intuitionistic fuzzy set; multi-attribute decision making method; maximum similarity optimization model similarity measure; interval-valued intuitionistic fuzzy set; multi-attribute decision making method; maximum similarity optimization model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ren, H.; Wang, G. An Interval-Valued Intuitionistic Fuzzy MADM Method Based on a New Similarity Measure. Information 2015, 6, 880-894.

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