Multi-criteria decision making involving uncertain information via fuzzy ranking and fuzzy aggregation functions

Many advances in artificial intelligence and machine learning are based on decision making, especially in uncertain settings. Due to its possible applications, decision making is currently a broad field of study in many areas like Computation, Economics and Business Management. The first techniques appeared in scenarios where information was modeled by real numbers. In all cases, one of the key steps in such processes was the summarization of the available information into a few values that helped the decision maker to complete this task. In this paper, we introduce a novel multi-criteria decision making methodology in the fuzzy context in which weights and experts' opinions (may be translated by linguistic labels) are stated as triangular fuzzy numbers. To do that, we take advantage of a recently presented fuzzy binary relation whose properties are according to human intuition and we carry out a study of the main properties that an aggregation function (a mapping to sum up information) must satisfy in the fuzzy framework. The presented procedure makes a final decision based on parabolic fuzzy numbers (not triangular). And this will be shown in an illustrative example.(c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study introduces a method for multi-criteria decision making in a fuzzy environment, where weights and experts' opinions are represented as triangular fuzzy numbers. By studying the main properties of aggregation functions in the fuzzy framework, a new decision making approach is adopted.