2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 1 , PP: 67-84, 2024 | Cite this article as | XML | Html | PDF
Silva A. Guido Javier 1 * , Juan G. Sailema Armijos 2 , Marco P. Villa Zura 3 , Maha Ibrahim 4
Doi: https://doi.org/10.54216/FPA.160106
This article explores the application of the linguistic 2-tuple computational model in decision-making processes, focusing on its efficiency in managing ambiguous and imprecise linguistic information, which is vital in complex decision-making environments. The main objective is to demonstrate the use of the Weighted Power Mean (WPM) operator for hierarchical aggregation, highlighting its adaptability in reflecting the priority structures of specific problems and preserving the integrity of expert opinions. The model enhances user interaction by minimizing the need for complex numerical conversions, facilitating more intuitive decision-making. The study introduces the methodology of the linguistic 2-tuples, emphasizing their practical application in various decision-making contexts through detailed case studies. It elaborates on the hierarchical aggregation model, discussing the flexibility and potential of the WPM operator to adjust the influence of individual criteria based on their importance. The article also examines potential improvements in aggregation operators to increase their effectiveness and applicability across different scenarios. This comprehensive analysis not only underscores the capabilities of linguistic computational models in modern decision-making environments but also proposes future directions for advancing these techniques to handle increasingly complex information landscapes.
Linguistic 2-tuples , Decision-making , Hierarchical aggregation , Weighted Power Mean (WPM) , Computational models.
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Leyva Vazquez, M. Y., Quiroz Martinez, M. A., Diaz Sanchez, J. H., & Aguilera Balseca, J. L. (2021). Decision model for qos in networking based on hierarchical aggregation of information. In Advances in Artificial Intelligence, Software and Systems Engineering: Proceedings of the AHFE 2020 Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, July 16-20, 2020, USA (pp. 361-368). Springer International Publishing
