DOI: 10.7763/IJCTE.2025.V17.1382
Logarithmic OWA Operators in Weighted Averages: Theoretical Advances and Decision-Making Applications
2. School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, 81 Broadway, Ultimo 2007, NSW, Australia
Email: gerardo.alfaro@umich.mx (G.G.A.-C.); victor.alfaro@umich.mx (V.G.A.-G.); jose.merigo@uts.edu.au (J.M.M.)
*Corresponding author
Manuscript received February 20, 2025; revised April 13, 2023; accepted August 27, 2025; published November 11, 2025
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.
Keywords—logarithmic aggregation operators, OWA operator, weighted average, generalized mean, sustainability index
Cite: Gerardo G. Alfaro-Calderón, Víctor G. Alfaro-García, and José M. Merigó, "Logarithmic OWA Operators in Weighted Averages: Theoretical Advances and Decision-Making Applications," International Journal of Computer Theory and Engineering, vol. 17, no. 4, pp. 202-211, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).