Title: A New Method for Intuitionistic Fuzzy Multiattribute Decision Making
Authors: Gupta, Pankaj
Lin, Chin-Teng
Mehlawat, Mukesh Kumar
Grover, Nishtha
Institute of Electrical and Control Engineering
Brain Research Center
Keywords: Fuzzy sets;intuitionistic fuzzy number;mathematical programming;multiattribute decision making (MADM);optimization method
Issue Date: Sep-2016
Abstract: In this paper, we study the multiattribute decision-making (MADM) problem with intuitionistic fuzzy values that represent information regarding alternatives on the attributes. Assuming that the weight information of the attributes is not known completely, we use an approach that utilizes the relative comparisons based on the advantage and disadvantage scores of the alternatives obtained on each attribute. The relative comparison of the intuitionistic fuzzy values in this research use all the three parameters, namely membership degree ("the more the better"), nonmembership degree ("the less the better"), and hesitancy degree ("the less the better"), thereby leading to the tradeoff values of all the three parameters. The score functions (advantage and disadvantage scores) used for this purpose are based on the positive contributions of these parameters, wherever applicable. Furthermore, these scores are used to obtain the strength and weakness scores leading to the satisfaction degrees of the alternatives. The optimal weights of the attributes are determined using a multiobjective optimization model that simultaneously maximizes the satisfaction degree of each alternative. The optimal solution is used for ranking and selecting the best alternative on the basis of the overall attribute values. To validate the proposed methodology, we present a numerical illustration of a real-world case. The methodology is further extended to treat MADM problem with interval-valued intuitionistic fuzzy information. Finally, a thorough comparison is done to demonstrate the advantages of the solution methodology over the existing methods used for the intuitionistic fuzzy MADM problems.
URI: http://dx.doi.org/10.1109/TSMC.2015.2478401
ISSN: 2168-2216
DOI: 10.1109/TSMC.2015.2478401
Volume: 46
Issue: 9
Begin Page: 1167
End Page: 1179
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