A New Method for Intuitionistic Fuzzy Multiattribute Decision Making

Gupta, P; Lin, CT; Mehlawat, MK; Grover, N

A New Method for Intuitionistic Fuzzy Multiattribute Decision Making

Gupta, P Lin, CT

Mehlawat, MK Grover, N

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 46 (9), pp. 1167 - 1179
Issue Date:: 2016-09-01

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Field	Value	Language
dc.contributor.author	Gupta, P	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Mehlawat, MK	en_US
dc.contributor.author	Grover, N	en_US
dc.date.issued	2016-09-01	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 46 (9), pp. 1167 - 1179	en_US
dc.identifier.issn	1083-4427	en_US
dc.identifier.uri	http://hdl.handle.net/10453/113463
dc.description.abstract	© 2013 IEEE. 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.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems	en_US
dc.relation.isbasedon	10.1109/TSMC.2015.2478401	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A New Method for Intuitionistic Fuzzy Multiattribute Decision Making	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	46	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	46	en_US

Abstract:

© 2013 IEEE. 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.

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http://hdl.handle.net/10453/113463