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Titre: A large scale consensus reaching process managing group hesitation
Auteur(s): Rodríguez, Rosa M.
Labella, Álvaro
De Tré, Guy
Martínez, Luis
Résumé: Nowadays 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.
Mots-clés: Large-scale group decision making
Consensus reaching process
Hesitant fuzzy sets
Sub-group weight
Intelligent consensus reaching process support system
Date de publication: nov-2018
metadata.dc.description.sponsorship: Spanish 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.
Editeur: Elsevier
Référence bibliographique: R.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.2018.06.009
Collection(s) :DI-Artículos

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