Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm

Yang, Z; Garg, H; Li, J; Srivastava, G; Cao, Z

Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm

Yang, Z Garg, H Li, J Srivastava, G Cao, Z

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2021
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Yang, Z
dc.contributor.author	Garg, H
dc.contributor.author	Li, J
dc.contributor.author	Srivastava, G
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.date.accessioned	2021-05-21T02:10:18Z
dc.date.available	2021-05-21T02:10:18Z
dc.date.issued	2021
dc.identifier.citation	Neural Computing and Applications, 2021
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/149032
dc.description.abstract	© 2020, Springer-Verlag London Ltd., part of Springer Nature. Q-rung orthopair fuzzy (q-ROF) set is one of the powerful tools for handling the uncertain multi-criteria decision-making (MCDM) problems, various MCDM methods under q-ROF environment have been developed in recent years. However, most existing studies merely concerned about the relationship between the criteria but they have not investigated the interactions between membership function and non-membership function. To explore the multiple heterogeneous relationships among membership functions and criteria, we propose a novel decision algorithm based on q-ROF set to deal with these using interactive operators and Maclaurin symmetric mean (MSM) operators. Specifically, the new interaction laws in the membership pairs of q-ROF sets are explained, and their properties are analyzed as the initial stage. Then, taking into account the influence of two or more factors on decision analysis, a q-ROF interaction Maclaurin symmetry mean (q-ROFIMSM) operator is formed based on the proposed interaction law to identify these factors’ interrelationship. Thirdly, based on the proposed operator with q-ROF information, a MCDM algorithm is developed and illustrated by numerical examples. An analysis of the feasibility, sensitivity, and superiority of the proposed framework is provided to validate our proposed method.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-020-05003-5
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-21T02:10:16Z
pubs.publication-status	Published

Abstract:

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Q-rung orthopair fuzzy (q-ROF) set is one of the powerful tools for handling the uncertain multi-criteria decision-making (MCDM) problems, various MCDM methods under q-ROF environment have been developed in recent years. However, most existing studies merely concerned about the relationship between the criteria but they have not investigated the interactions between membership function and non-membership function. To explore the multiple heterogeneous relationships among membership functions and criteria, we propose a novel decision algorithm based on q-ROF set to deal with these using interactive operators and Maclaurin symmetric mean (MSM) operators. Specifically, the new interaction laws in the membership pairs of q-ROF sets are explained, and their properties are analyzed as the initial stage. Then, taking into account the influence of two or more factors on decision analysis, a q-ROF interaction Maclaurin symmetry mean (q-ROFIMSM) operator is formed based on the proposed interaction law to identify these factors’ interrelationship. Thirdly, based on the proposed operator with q-ROF information, a MCDM algorithm is developed and illustrated by numerical examples. An analysis of the feasibility, sensitivity, and superiority of the proposed framework is provided to validate our proposed method.

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http://hdl.handle.net/10453/149032