Triangular Fuzzy Partitioned Bonferroni Mean Operators and Their Application to Multiple Attribute Decision Making

Verma, R; Merigó, JM; Mittal, N

Triangular Fuzzy Partitioned Bonferroni Mean Operators and Their Application to Multiple Attribute Decision Making

Verma, R Merigó, JM Mittal, N

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 941-949
Issue Date:: 2019-01-28

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Field	Value	Language
dc.contributor.author	Verma, R
dc.contributor.author	Merigó, JM
dc.contributor.author	Mittal, N
dc.date	2018-11-18
dc.date.accessioned	2020-05-31T21:57:43Z
dc.date.available	2020-05-31T21:57:43Z
dc.date.issued	2019-01-28
dc.identifier.citation	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 941-949
dc.identifier.isbn	9781538692769
dc.identifier.uri	http://hdl.handle.net/10453/141016
dc.description.abstract	© 2018 IEEE. The Bonferroni mean (BM) operator, introduced by Bonferroni, is a powerful tool to capture the interrelationship among aggregated arguments. Various generalizations and extensions of BM have developed and applied to solve many realworld problems. Recently, the notion of Partitioned Bonferroni mean (PBM) operator has been proposed with the assumption that the interrelationships do not always exist among all of the attributes. This work studies the PBM operator under triangular fuzzy environment. First, we propose a new fuzzy aggregation operator called the triangular fuzzy partitioned Bonferroni mean} (TFPBM) operator for aggregating triangular fuzzy numbers. Some properties and special cases of the new aggregation operator are also investigated. For the situations where the input arguments have different importance, we then define the triangular fuzzy weighted partitioned Bonferroni mean} (TFWPBM) operator. Furthermore, based on TFWPBM operator, an approach to deal with multiple attribute decision-making problems under triangular fuzzy environment is developed. Finally, a practical example is provided to illustrate the developed approach.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
dc.relation.ispartof	2018 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.relation.isbasedon	10.1109/SSCI.2018.8628728
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Triangular Fuzzy Partitioned Bonferroni Mean Operators and Their Application to Multiple Attribute Decision Making
dc.type	Conference Proceeding
utslib.for	0802 Computation Theory and Mathematics
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 Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-05-31T21:57:37Z
pubs.finish-date	2018-11-21
pubs.publication-status	Published
pubs.start-date	2018-11-18
utslib.start-page	941

Abstract:

© 2018 IEEE. The Bonferroni mean (BM) operator, introduced by Bonferroni, is a powerful tool to capture the interrelationship among aggregated arguments. Various generalizations and extensions of BM have developed and applied to solve many realworld problems. Recently, the notion of Partitioned Bonferroni mean (PBM) operator has been proposed with the assumption that the interrelationships do not always exist among all of the attributes. This work studies the PBM operator under triangular fuzzy environment. First, we propose a new fuzzy aggregation operator called the triangular fuzzy partitioned Bonferroni mean} (TFPBM) operator for aggregating triangular fuzzy numbers. Some properties and special cases of the new aggregation operator are also investigated. For the situations where the input arguments have different importance, we then define the triangular fuzzy weighted partitioned Bonferroni mean} (TFWPBM) operator. Furthermore, based on TFWPBM operator, an approach to deal with multiple attribute decision-making problems under triangular fuzzy environment is developed. Finally, a practical example is provided to illustrate the developed approach.

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http://hdl.handle.net/10453/141016