The uncertain ordered weighted averaging adequacy coefficient operator

Figuerola-Wischke, A; Gil-Lafuente, AM; Merigó, JM

The uncertain ordered weighted averaging adequacy coefficient operator

Figuerola-Wischke, A Gil-Lafuente, AM Merigó, JM

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: International Journal of Approximate Reasoning, 2022, 148, pp. 68-79
Issue Date:: 2022-09-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (346.28 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Figuerola-Wischke, A
dc.contributor.author	Gil-Lafuente, AM
dc.contributor.author	Merigó, JM
dc.date.accessioned	2023-03-21T23:27:38Z
dc.date.available	2023-03-21T23:27:38Z
dc.date.issued	2022-09-01
dc.identifier.citation	International Journal of Approximate Reasoning, 2022, 148, pp. 68-79
dc.identifier.issn	0888-613X
dc.identifier.uri	http://hdl.handle.net/10453/168004
dc.description.abstract	This article introduces the uncertain ordered weighted averaging adequacy coefficient (UOWAAC) operator. This novel operator uses the ordered weighted averaging (OWA) operator, the adequacy coefficient, and the interval numbers in a single formulation. This article also extends the UOWAAC operator by using order-inducing variables in the reordering process of the input arguments. This new extension is called the uncertain induced ordered weighted averaging adequacy coefficient (UIOWAAC) operator. The article also presents an application of the new approach in a multi-criteria group decision making (MCGDM) problem about international expansion. In addition, a comparative analysis is conducted with the purpose of demonstrating the superiority of the UOWAAC and UIOWAAC aggregation operators in specific situations. Likewise, the use of basic uncertain information (BUI) is discussed. The results show the usefulness of these new aggregation operators in real-life decision making problems under uncertainty, particularly when the decision maker wants to compare different alternatives with an ideal but without giving any penalty or reward in the case that the ideal levels are exceeded.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	International Journal of Approximate Reasoning
dc.relation.isbasedon	10.1016/j.ijar.2022.06.001
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0103 Numerical and Computational Mathematics, 0104 Statistics, 0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	The uncertain ordered weighted averaging adequacy coefficient operator
dc.type	Journal Article
utslib.citation.volume	148
utslib.for	0103 Numerical and Computational Mathematics
utslib.for	0104 Statistics
utslib.for	0801 Artificial Intelligence and Image Processing
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	open_access	*
dc.date.updated	2023-03-21T23:27:37Z
pubs.publication-status	Published
pubs.volume	148

Abstract:

This article introduces the uncertain ordered weighted averaging adequacy coefficient (UOWAAC) operator. This novel operator uses the ordered weighted averaging (OWA) operator, the adequacy coefficient, and the interval numbers in a single formulation. This article also extends the UOWAAC operator by using order-inducing variables in the reordering process of the input arguments. This new extension is called the uncertain induced ordered weighted averaging adequacy coefficient (UIOWAAC) operator. The article also presents an application of the new approach in a multi-criteria group decision making (MCGDM) problem about international expansion. In addition, a comparative analysis is conducted with the purpose of demonstrating the superiority of the UOWAAC and UIOWAAC aggregation operators in specific situations. Likewise, the use of basic uncertain information (BUI) is discussed. The results show the usefulness of these new aggregation operators in real-life decision making problems under uncertainty, particularly when the decision maker wants to compare different alternatives with an ideal but without giving any penalty or reward in the case that the ideal levels are exceeded.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/168004