A fuzzy closeness approach to fuzzy multi-attribute decision making |
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Authors: | D -F Li |
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Institution: | (1) Department of Sciences, Shenyang Institute of Aeronautical Engineering, Shenyang, Liaoning, 110034, China;(2) Department Five, Dalian Naval Academy, No. 1, Xiaolong Street, Dalian, Liaoning, 116018, China;(3) Department of Command Engineering, Missile Institute, Air Force Engineering University, Xi’an, Shaanxi, 713800, China |
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Abstract: | The aim of this paper is to develop a new fuzzy closeness (FC) methodology for multi-attribute decision making (MADM) in fuzzy
environments, which is an important research field in decision science and operations research. The TOPSIS method based on
an aggregating function representing “closeness to the ideal solution” is one of the well-known MADM methods. However, while
the highest ranked alternative by the TOPSIS method is the best in terms of its ranking index, this does not mean that it
is always the closest to the ideal solution. Furthermore, the TOPSIS method presumes crisp data while fuzziness is inherent
in decision data and decision making processes, so that fuzzy ratings using linguistic variables are better suited for assessing
decision alternatives. In this paper, a new FC method for MADM under fuzzy environments is developed by introducing a multi-attribute
ranking index based on the particular measure of closeness to the ideal solution, which is developed from the fuzzy weighted
Minkowski distance used as an aggregating function in a compromise programming method. The FC method of compromise ranking
determines a compromise solution, providing a maximum “group utility” for the “majority” and a minimum individual regret for
the “opponent”. A real example of a personnel selection problem is examined to demonstrate the implementation process of the
method proposed in this paper. |
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Keywords: | Decision analysis Fuzzy closeness method TOPSIS Linguistic variable Fuzzy set |
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