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A Multi-Attribute Decision-Making Model Using Interval-Valued Intuitionistic Fuzzy Numbers and Attribute Correlation
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Author(s): Sha Fu (School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, China), Xi-long Qu (School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, China), Hang-jun Zhou (School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, China)and Guo-bing Fan (School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, China)
Copyright: 2018
Volume: 14
Issue: 1
Pages: 14
Source title:
International Journal of Enterprise Information Systems (IJEIS)
Editor(s)-in-Chief: Gianluigi Viscusi (Linköping University, Sweden)
DOI: 10.4018/IJEIS.2018010102
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Abstract
This article describes a decision analysis method with attribute correlation and attribute values of interval-valued intuitionistic fuzzy numbers (IVIFN). This considers the correlation between attributes and undetermined attribute weight information was put forward against the case where solution attribute values are interval-valued intuitionistic fuzzy numbers. To achieve a comprehensive consideration of the undetermined evaluation attribute weight information, experts' positive and negative ideal solutions against each attribute are taken as reference points, attribute weight is obtained via decision making trial and evaluation laboratory (DEMATEL) method according to the direct relation matrix provided by experts against attribute sets. On this basis, evaluation information is aggregated by using weighted arithmetic average operators. Then, the closeness degree formula of interval-valued intuitionistic fuzzy numbers relative to the maximum IVIFN is provided by combining the Euclidean distance of IVIFN, thus determining closeness degree and making an ordering of all solutions. Finally, the feasibility and validity of the method proposed in this study are verified via a case analysis of the best information system decision-making.
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