- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
The hesitant fuzzy linguistic term set (HFLTS) has gained great success as it can be used to represent several linguistic terms or comparative linguistic expressions together with some context-free grammars. This new approach has enabled the analysis and computing of linguistic expressions with uncertainties and opened the door for the possibility to develop more comprehensive and powerful decision theories and methods based on linguistic knowledge. Lots of new approaches and proposals for decision-making problems have been proposed to overcome the limitations of previous linguistic decision-making approaches. Now and in the future, decision-making methodologies and algorithms with hesitant fuzzy linguistic models would be a quite promising research line representing a high-quality breakthrough in this topic. To facilitate the study on HFLTS theory, this paper makes a state-of-the-art survey on HFLTSs based on the 134 selected papers from Web of Sciences published from January 2012 to October 2017. We justify the motivation, definitions, operations, comparison methods and measures of HFLTSs. We also summarize the different extensions of HFLTSs. The studies on multiple criteria decision making (MCDM) with HFLTSs in terms of aggregation operators and MCDM methods are clearly reviewed. We also conduce some overviews on decision making with hesitant fuzzy linguistic preference relations. The applications, research challenges and future directions are also given.
In this paper, we have made an overview on the state of the art of the researches on hesitant fuzzy linguistic theory during the period from 2012 to 2017 based on the selected 134 papers from the well-known database, Web of Sciences. The motivation, definitions and operations have been clearly summarized. As the comparison methods between HFLEs are essential for many decision-making methods, eight different comparison schemes have been reviewed in-depth. We have summerized the measures of HFLTSs. We have described all the distinct extensions on HFLTSs. We have conducted a survey on MCDM with HFLTSs in terms of aggregation operators and MCDM methods. We have made an overview on decision making with HFLPRs. The applications, research challenges and future directions have also been given.