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dc.contributor.authorMorente-Molinera, Juan Antonio (1)
dc.contributor.authorKou, G
dc.contributor.authorGonzález-Crespo, Rubén (1)
dc.contributor.authorCorchado, J M
dc.contributor.authorHerrera-Viedma, Enrique
dc.date2017-12
dc.date.accessioned2018-03-07T16:30:58Z
dc.date.available2018-03-07T16:30:58Z
dc.identifier.issn0950-7051
dc.identifier.urihttps://reunir.unir.net/handle/123456789/6328
dc.description.abstractClassic multi-criteria group decision making models that have a high amount of alternatives are unmanageable for the experts. This is because they have to provide one value per each alternative and criteria. In this paper, we focus on solving this issue by carrying out multi-criteria group decision making methods using a different novel approach. Concretely, fuzzy ontologies reasoning procedures are used in order to automatically obtain the alternatives ranking classification. Thanks to our novel methodology, experts only need to provide the importance of a small set of criteria values making it possible for experts to perform multi-criteria group decision making procedures that have a high amount of alternatives without having to directly deal with them. Furthermore, in order to allow experts to provide their preferences in a comfortable way, multi-granular fuzzy linguistic modelling is used in order to allow each expert to choose the linguistic label set that better fits him/her.es_ES
dc.language.isoenges_ES
dc.publisherKnowledge-Based Systemses_ES
dc.relation.ispartofseries;vol. 137
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0950705117303891es_ES
dc.rightsrestrictedAccesses_ES
dc.subjectfuzzy linguistic modellinges_ES
dc.subjectgroup decision makinges_ES
dc.subjectcomputing with wordses_ES
dc.subjectmulti-criteria decision makinges_ES
dc.subjectfuzzy ontologieses_ES
dc.subjectScopuses_ES
dc.subjectJCRes_ES
dc.titleSolving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methodses_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1016/j.knosys.2017.09.010


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