A novel linguistic representative model based on discrete fuzzy numbers for multi-granularity linguistic decision-making

Many decision-making problems use vague and imprecise information in linguistic variable formats as preferences. In this paper, we present a linguistic representative model based on discrete fuzzy numbers whose support is a subset of consecutive natural numbers. The arbitrary linguistic term is defined to give decision makers more freedom to express their preferences. The discrete fuzzy weighted normal operators defined on a finite chain in accordance with the granularity of linguistic term set are used to complete the aggregation process.