Personalized fuzzy semantic model of PHFLTS: Application to linguistic group decision making
The proportional hesitant fuzzy linguistic term set (PHFLTS) has been effectively employed in analyzing the group’s hesitancy in linguistic group decision making (LGDM). The application of PHFLTS assists in capturing the individual’s hesitancy across diverse time periods. It is acknowledged that a s...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:dnet:ruja________::9fb8d0b461c6bf98dffa1c808bda4761 |
| Acceso en línea: | https://hdl.handle.net/10953/7833 |
| Access Level: | acceso abierto |
| Palabra clave: | Computing with words Linguistic group decision making Proportional hesitant fuzzy linguistic term set Personalized individual semantic 004.8 |
| Sumario: | The proportional hesitant fuzzy linguistic term set (PHFLTS) has been effectively employed in analyzing the group’s hesitancy in linguistic group decision making (LGDM). The application of PHFLTS assists in capturing the individual’s hesitancy across diverse time periods. It is acknowledged that a single word could potentially convey various meanings to different decision makers, such differences can be proficiently managed by utilizing personalized individual semantic (PIS) models. Previous approaches for calculating PIS failed to incorporate the individual’s updating preference information over time, which increases the risk that the computation of PIS is affected by random factors in a specific moment. In our current research, individual linguistic preference gathered over a time period are leveraged to form the PHFLTS. Additionally, a consistency driven optimization model based on PHFLTS is formulated to obtain PIS of linguistic terms. Subsequently, a fuzzy representation model termed as the fuzzy envelope of PHFLTS is introduced to facilitate the computation with words processes, integrating PHFLTS in LGDM. The practicality and legitimacy of these proposed models are evaluated through a comparative analysis. Lastly, these proposed models are tested and applied in a dedicated case study to further prove their usefulness and efficacy. |
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