Extension of TOPSIS for Multiple Attribute Decision Making Using Intuitionistic Fuzzy Sets

Abstract:

This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document. Due to the nature of vagueness inherent to real-life situations, some fuzzy data are deemed to suitable enough to describe the qualitative and/or quantitative estimation for decision making problems. Therefore, a new method for multiple attribute decision making under fuzzy environment is discussed, in which the attribute values take the form of intuitionistic fuzzy numbers. To overcome some disadvantages of existing distance measures like indiscrimination, counterintuitive results and difficulty of interpretation, we introduce a new class of distance for describing the deviation degrees between intuitionistic fuzzy sets. Furthermore, the measure of similarity degree for each alternative to ideal point is calculated through using the new proposed fuzzy distance. A model of TOPSIS is designed with the introduction of the particular closeness coefficient composed of similarity degrees. Then, we extend the TOPSIS method to aggregate the fuzzy information corresponding to each alternative, and rank the alternatives according to their closeness coefficients. Finally, an illustrative example is given to demonstrate the proposed approach practicality and effectiveness.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 4053-4058
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.4053
• Online since: January 2012
• Authors: Yuan Yuan, Li Yang He
• Keywords: Alternative Ranking, Intuitionistic Fuzzy Number, Multiple Attribute Decision Making, Topsis Method

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