Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment

TOPSIS is one of the well-known methods for multiple attribute decision making (MADM). In this paper, we extend the TOPSIS method to solve multiple attribute group decision making (MAGDM) problems in interval-valued intuitionistic fuzzy environment in which all the preference information provided by...

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Bibliographic Details
Published in:Applied mathematical modelling 2011-05, Vol.35 (5), p.2544-2556
Main Authors: Park, Jin Han, Park, Il Young, Kwun, Young Chel, Tan, Xuegong
Format: Article
Language:English
Subjects:
Applied sciences
Decision theory. Utility theory
Exact sciences and technology
Interval-valued intuitionistic fuzzy decision matrix
Interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator
Interval-valued intuitionistic fuzzy number (IVIFN)
Multiple attribute group decision making (MAGDM)
Operational research and scientific management
Operational research. Management science
TOPSIS
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Summary:TOPSIS is one of the well-known methods for multiple attribute decision making (MADM). In this paper, we extend the TOPSIS method to solve multiple attribute group decision making (MAGDM) problems in interval-valued intuitionistic fuzzy environment in which all the preference information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFNs), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy decision matrices provided by the decision-makers into the collective interval-valued intuitionistic fuzzy decision matrix, and then we use the score function to calculate the score of each attribute value and construct the score matrix of the collective interval-valued intuitionistic fuzzy decision matrix. From the score matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and construct the weighted collective interval-valued intuitionistic fuzzy decision matrix, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. Based on different distance definitions, we calculate the relative closeness of each alternative to the interval-valued intuitionistic positive-ideal solution and rank the alternatives according to the relative closeness to the interval-valued intuitionistic positive-ideal solution and select the most desirable one(s). Finally, an example is used to illustrate the applicability of the proposed approach.
ISSN:0307-904X
DOI:10.1016/j.apm.2010.11.025