Paper Info
Title
TOPSIS method for intuitionistic fuzzy multiple-criteria decision making and its application to investment selection.
Abstract
Purpose - The purpose of this paper is to present a hybrid intuitionistic fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method, called intuitionistic fuzzy ordered weighted averaging weighted averaging (OWAWA) distance TOPSIS (IFOWAWAD-TOPSIS) method for intuitionistic fuzzy multiple-criteria decision making (MCDM) problems. Design/methodology/approach - Based on the OWAWA operator, the authors develop the intuitionistic fuzzy OWAWA distance measure, then the IFOWAWAD-TOPSIS method is obtained by using the IFOWAWAD and traditional TOPSIS. Findings - The developed IFOWAWAD-TOPSIS method can overcome the drawback of traditional TOPSIS method that cannot consider both the subjective information of attributes and the attitudinal character of decision maker. Research limitations/implications - Clearly, this paper is devoted to the OWA operator, MCDM and intuitionistic fuzzy theory. Practical implications - The developed method is applicable in a wide range of situations such as decision-making, statistics, engineering and economics. A numerical example concerning investment selection is given to illustrate the practicability and usefulness of the proposed approach. Originality/value - This paper fulfils an identified need to study how to make a decision considering both the subjective information of attribute and the attitudinal character of decision maker in intuitionistic fuzzy environment.
Year
DOI
Venue
2016
10.1108/K-04-2015-0093
KYBERNETES
Keywords
Field
DocType
Decision making,Operational research,Fuzzy logic
Mathematical optimization,Multiple-criteria decision analysis,Multiple criteria,Computer science,Fuzzy logic,Ideal solution,Operator (computer programming),Artificial intelligence,TOPSIS,Decision maker
Journal
Volume
Issue
ISSN
45
2
0368-492X
Citations 
PageRank 
References 
23
0.73
24
Authors
2
Name
Order
Citations
PageRank
Shouzhen Zeng1230.73
Yao Xiao2649.74