Methods of ranking for aggregated fuzzy numbers from interval-valued data

TL;DR

This paper introduces two novel ranking methods for aggregated fuzzy numbers derived from interval data, enhancing existing similarity measures and applying them within a modified TOPSIS framework for improved decision-making.

Contribution

It proposes new ranking techniques for aggregated fuzzy numbers from interval data, addressing limitations of previous measures and integrating them into a modified TOPSIS method.

Findings

Proposed ranking methods outperform existing measures in synthetic tests.

Application of methods to real-world data demonstrates practical effectiveness.

Modified TOPSIS algorithm improves decision accuracy with aggregated fuzzy numbers.

Abstract

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.