Una nueva metodología de toma de decisiones multi-criterio utilizando información imprecisa vía ranking difuso y funciones de agregación difusas

Abstract

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 eld 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 (maybe 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 an 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). In addition, we will show an illustrative example about how to apply these algebraic tools by proposing a new fuzzy Delphi methodology to reach consensus among experts.