Journal
FUZZY OPTIMIZATION AND DECISION MAKING
Volume 6, Issue 3, Pages 237-254Publisher
SPRINGER
DOI: 10.1007/s10700-007-9010-1
Keywords
decision analysis; fuzzy closeness method; TOPSIS; linguistic variable; fuzzy set
Funding
- Natural Science Foundation of China [70571086]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China [2005-546]
- China Postdoctoral Science Foundation [2005-18]
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The aim of this paper is to develop a newfuzzy 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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