Logarithmic OWA Operators in Weighted Averages: Theoretical Advances and Decision-Making Applications

Alfaro-Calderón, GG; Alfaro-García, VG; Merigó, JM

Logarithmic OWA Operators in Weighted Averages: Theoretical Advances and Decision-Making Applications

Alfaro-Calderón, GG Alfaro-García, VG Merigó, JM

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Publisher:: IACSIT Press
Publication Type:: Journal Article
Citation:: International Journal of Computer Theory and Engineering, 2025, 17, (4), pp. 202-211
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Alfaro-Calderón, GG
dc.contributor.author	Alfaro-García, VG
dc.contributor.author	Merigó, JM
dc.date.accessioned	2026-03-23T03:47:20Z
dc.date.available	2026-03-23T03:47:20Z
dc.date.issued	2025-01-01
dc.identifier.citation	International Journal of Computer Theory and Engineering, 2025, 17, (4), pp. 202-211
dc.identifier.issn	1793-8201
dc.identifier.uri	http://hdl.handle.net/10453/194422
dc.description.abstract	Aggregation operators are essential in multi-attribute decision-making, particularly for managing uncertainty and risk. Traditional methods, such as the Ordered Weighted Averaging (OWA) operator, typically address either probability-based weighting or uncertainty-based reordering, but rarely combine both within a unified framework. This paper proposes the Ordered Weighted Logarithmic Averaging Weighted Average (OWLAWA) operator, a novel approach that merges the benefits of weighted averaging and ordered reordering with a logarithmic transformation to better reflect decision-maker preferences under uncertainty. The theoretical properties of this operator including monotonicity, boundedness, and commutativity are formally established. A multi-attribute decision-making framework is then presented, integrating recognized expert weighting methods, including an entropy-based approach, to enhance decision robustness. Through comparative analysis and a sustainability-focused case study involving 20 companies, results demonstrate that the proposed approach yields a controlled sub valuation effect, particularly beneficial in risk-sensitive or compliance-driven environments. These findings indicate a more adaptive and structured decision-making process relative to conventional operators, accommodating both structured probabilities and uncertain preferences. By unifying risk-based and uncertainty-based weighting within a logarithmic formulation, this operator offers a versatile and structured tool for applications in financial risk management, policy evaluation, and supply chain optimization. Future research may explore its integration with fuzzy systems and machine learning methods, further expanding its adaptability in complex decision scenarios.
dc.language	en
dc.publisher	IACSIT Press
dc.relation.ispartof	International Journal of Computer Theory and Engineering
dc.relation.isbasedon	10.7763/IJCTE.2025.V17.1382
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0805 Distributed Computing
dc.subject.classification	4613 Theory of computation
dc.title	Logarithmic OWA Operators in Weighted Averages: Theoretical Advances and Decision-Making Applications
dc.type	Journal Article
utslib.citation.volume	17
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0805 Distributed Computing
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.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-03-23T03:47:18Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	4

Abstract:

Aggregation operators are essential in multi-attribute decision-making, particularly for managing uncertainty and risk. Traditional methods, such as the Ordered Weighted Averaging (OWA) operator, typically address either probability-based weighting or uncertainty-based reordering, but rarely combine both within a unified framework. This paper proposes the Ordered Weighted Logarithmic Averaging Weighted Average (OWLAWA) operator, a novel approach that merges the benefits of weighted averaging and ordered reordering with a logarithmic transformation to better reflect decision-maker preferences under uncertainty. The theoretical properties of this operator including monotonicity, boundedness, and commutativity are formally established. A multi-attribute decision-making framework is then presented, integrating recognized expert weighting methods, including an entropy-based approach, to enhance decision robustness. Through comparative analysis and a sustainability-focused case study involving 20 companies, results demonstrate that the proposed approach yields a controlled sub valuation effect, particularly beneficial in risk-sensitive or compliance-driven environments. These findings indicate a more adaptive and structured decision-making process relative to conventional operators, accommodating both structured probabilities and uncertain preferences. By unifying risk-based and uncertainty-based weighting within a logarithmic formulation, this operator offers a versatile and structured tool for applications in financial risk management, policy evaluation, and supply chain optimization. Future research may explore its integration with fuzzy systems and machine learning methods, further expanding its adaptability in complex decision scenarios.

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http://hdl.handle.net/10453/194422