Abstract
The linguistic approaches are required in order to assess qualitative aspects of many real problems. In most of these problems, decision makers only adopt single and very simple terms which would not reflect exactly what the experts mean for many intricate applications. Frequently, the assessments of decision making problems involve comparative linguistic expressions. Accordingly, we propose a novel distance measure between hesitant fuzzy linguistic term sets (HFLTSs) to solve fuzzy group decision making (FGDM) problems. Firstly we define the characteristic functions to describe the HFLTSs transformed from comparative linguistic expressions. Then we construct a weighted HFLTSs graph containing all notes in the HFLTSs. Distances in the graph of individual assessments are defined by measures considering diversity and specificity of HFLTS’s granularity. We put forward a new approach to achieve aggregation results for group decision making to realize the minimal distances with individual assessments. Finally, numerical examples are illustrated.
Highlights
Decision making is a common process to human beings
Inspired by decision making models based on distance measures, we look for other measures, based on fuzzy sets and fuzzy logic, to identify the differences of hesitant fuzzy linguistic term sets (HFLTSs) to avoid complex computation
We focus on investigating the distance measures for HFLTSs, and their properties
Summary
Decision making is a common process to human beings. Decision making problems are usually defined in uncertain and imprecise situations. Zhao [15] introduced distance measures for extended HFLTS (EHFLTS) and developed a novel multi-criteria group decision making model. Wang and Xu [28] proposed two distinct consistency measures of extended hesitant fuzzy linguistic preference relations for group decision making The assumptions such as the equality among the distances between consecutive linguistic terms for all the agents [27], and the assumption that distance measures only be used to solve the MCDM problem with single expert/decision maker [26], will limit their scope of application. Inspired by decision making models based on distance measures, we look for other measures, based on fuzzy sets and fuzzy logic, to identify the differences of HFLTSs to avoid complex computation.
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