Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/1787
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dc.contributor.authorRodríguez, Rosa M.-
dc.contributor.authorLabella, Álvaro-
dc.contributor.authorDe Tré, Guy-
dc.contributor.authorMartínez, Luis-
dc.date.accessioned2024-01-31T08:11:07Z-
dc.date.available2024-01-31T08:11:07Z-
dc.date.issued2018-11-
dc.identifier.citationR.M. Rodríguez, Á. Labella, G. De Tré, L. Martínez, A large scale consensus reaching process managing group hesitation. Knowledge-Based Systems, vol. 159, n.º 1, pp. 86-97, 2018. 10.1016/j.knosys.2018.06.009es_ES
dc.identifier.issn0950-7051es_ES
dc.identifier.other10.1016/j.knosys.2018.06.009es_ES
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S0950705118303137es_ES
dc.identifier.urihttps://hdl.handle.net/10953/1787-
dc.description.abstractNowadays due to the social networks and the technological development, large-scale group decision making (LSGDM) problems are fairly common and decisions that may affect to lots of people or even the society are better accepted and more appreciated if they agreed. For this reason, consensus reaching processes (CRPs) have attracted researchers attention. Although, CRPs have been usually applied to GDM problems with a few experts, they are even more important for LS-GDM, because differences among a big number of experts are higher and achieving agreed solutions is much more complex. Therefore, it is necessary to face some challenges in LS-GDM. This paper presents a new adaptive CRP model to deal with LS-GDM which includes: (i) a clustering process to weight experts’ sub-groups taking into account their size and cohesion, (ii) it uses hesitant fuzzy sets to fuse expert’s sub-group preferences to keep as much information as possible and (iii) it defines an adaptive feedback process that generates advice depending on the consensus level achieved to reduce the time and supervision costs of the CRP. Additionally, the proposed model is implemented and integrated in an intelligent CRP support system, so-called AFRYCA 2.0 to carry out this new CRP on a case study and compare it with existing models.es_ES
dc.description.sponsorshipSpanish National research project TIN2015-66524-P, Spanish Ministry of Economy and Finance Postdoctoral fellow (IJCI-2015-23715), Spanish mobility program Jose Castillejo (CAS15/00047) and ERDF.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.ispartofKnowledge-Based Systems [2018]; [159]:[86-97]es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectLarge-scale group decision makinges_ES
dc.subjectConsensus reaching processes_ES
dc.subjectClusteringes_ES
dc.subjectHesitant fuzzy setses_ES
dc.subjectSub-group weightes_ES
dc.subjectIntelligent consensus reaching process support systemes_ES
dc.titleA large scale consensus reaching process managing group hesitationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
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