Methods of ranking for aggregated fuzzy numbers from interval-valued data
This work addresses the problem of effectively ranking fuzzy numbers for decision-making processes in scenarios involving interval-valued data, which is relevant for researchers and practitioners working with fuzzy sets and multi-criteria decision analysis.
This paper introduces two methods for ranking aggregated fuzzy numbers derived from interval-valued data, utilizing the Interval Agreement Approach (IAA). The proposed methods integrate existing similarity measures with new attributes specific to aggregated fuzzy numbers, demonstrating improvements over prior measures in both synthetic and real-world applications.
This paper primarily presents two methods of ranking aggregated fuzzy numbers from intervals using the Interval Agreement Approach (IAA). The two proposed ranking methods within this study contain the combination and application of previously proposed similarity measures, along with attributes novel to that of aggregated fuzzy numbers from interval-valued data. The shortcomings of previous measures, along with the improvements of the proposed methods, are illustrated using both a synthetic and real-world application. The real-world application regards the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm, modified to include both the previous and newly proposed methods.