Hesitant Fuzzy Linguistic Terms Sets for Decision Making
Dealing with uncertainty is always a challenging problem, and different tools have been proposed to deal with it. Recently, a new model that is based on hesitant fuzzy sets has been presented to manage situations in which experts hesitate between several values to assess an indicator, alternative, v...
| Authors: | , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2012 |
| Country: | España |
| Institution: | Universidad de Jaén |
| Repository: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:dnet:ruja________::ca4a79ef969f32f02bf5ae85afd601c7 |
| Online Access: | https://ieeexplore.ieee.org/document/6030926 https://hdl.handle.net/10953/1761 |
| Access Level: | Open access |
| Keyword: | Context-free grammar fuzzy linguistic approach hesitant fuzzy sets linguistic decision making linguistic information |
| Summary: | Dealing with uncertainty is always a challenging problem, and different tools have been proposed to deal with it. Recently, a new model that is based on hesitant fuzzy sets has been presented to manage situations in which experts hesitate between several values to assess an indicator, alternative, variable, etc. Hesitant fuzzy sets suit the modeling of quantitative settings; however, similar situations may occur in qualitative settings so that experts think of several possible linguistic values or richer expressions than a single term for an indicator, alternative, variable, etc. In this paper, the concept of a hesitant fuzzy linguistic term set is introduced to provide a linguistic and computational basis to increase the richness of linguistic elicitation based on the fuzzy linguistic approach and the use of context-free grammars by using comparative terms. Then, a multicriteria linguistic decision-makingmodel is presented in which experts provide their assessments by eliciting linguistic expressions. This decision model manages such linguistic expressions by means of its representation using hesitant fuzzy linguistic term sets. |
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