A Linguistic 2-Tuple Multicriteria Decision Making Model dealing with Hesitant Linguistic Information
Tipo de publicación: International Conference
Año de publicación: 2015
Autores: Rosa María Rodríguez
Director: Luis Martínez, Francisco Herrera
Tipo: IEEE International Conference on Fuzzy Systems
Editorial: IEEE Computational Intelligence Society
Fecha de publicación: August 2-5th
Lugar de publicación: Istambul (Turkey)
Resumen: Decision making has become a core research area in different fields such as evaluation, engineering, medicine, etc. Usually, decision making problems are defined in contexts with vague and imprecise information. The use of linguistic modeling has provided successful results in decision making problems. However, most of the linguistic approaches are limited, because they restrict the elicitation of linguistic information to single linguistic terms and sometimes due to the lack of information, time or knowledge, decision makers hesitate among several linguistic terms to elicit their assessments and the use of only one linguistic term cannot reflect their assessments in a proper way. Therefore, more elaborated expressions than single linguistic terms might support decision makers in such hesitant situations and improve the elicitation of hesitant linguistic information. The use of hesitant fuzzy linguistic term sets (HFLTS), allows modeling this hesitation and facilitates the generation of comparative linguistic expressions similar to the expressions used by human beings in real world decision making problems using context-free grammars. There are different decision making models that deal with HFLTS, however they do not provide linguistic results as the computing with words scheme proposed to facilitate their comprehension. Therefore, the aim of this contribution is to present a multicriteria decision making model that not only improves the elicitation of hesitant linguistic information, but also obtains linguistic results easy to understand by decision makers. To achieve this latter goal the proposed model will make use of the linguistic 2-tuple model
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