Examinando por Autor "Dutta, Bapi"
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Ítem An optimal Best-Worst prioritization method under a 2-tuple linguistic environment in decision making(ELSEVIER, 2021-05) Labella, Álvaro; Dutta, Bapi; Martínez, LuisMulti-criteria group decision making (MCGDM) deals with decision makers who evaluate alternatives over several criteria. MCGDM problems evolve in tandem with the progress of our society. Such progress has given rise to the large-scale group decision making (LS-GDM) problems in which hundreds of decision makers may participate in the decision process and new challenges to face such as groups’ formation and polarization opinions. Most real world MCGDM problems present changing contexts with uncertainty that cannot be modeled by numerical values. Under these circumstances, the use of linguistic variables and computing with words (CW) processes have provided successfully results. Concretely, the 2-tuple linguistic computational model stands out because its precise linguistic computations and high interpretability. On the other hand, pairwise comparison is a widely used elicitation technique in MCGDM, but a large number of comparisons might lead inconsistent decision makers’ preferences. The Best-Worst method (BWM) reduces the number of pairwise comparisons and the inconsistency in decision makers’ opinions. Several BWM approaches have been proposed to manage linguistic information but none of them take advantage of the 2-tuple linguistic computational process based on the CW approach, which would allow to obtain precise and understandable results. This paper aims to present an extended 2-tuple BWM to reduce the number of pairwise comparisons in MCGDM problems and model the uncertainty associated with them to accomplish accuracy computations and obtaining interpretable results. Moreover, we apply our proposal to LS-GDM scenarios in which polarization opinions and sub-groups identification, ignored from any of BWM proposals, are considered. Finally, the new model is applied to several illustrative MCGDM problems.Ítem Comprehensive minimum cost models for large scale group decision making with consistent fuzzy preference relations(Elsevier, 2021-03) Rodríguez, Rosa M.; Labella, Álvaro; Dutta, Bapi; Martínez, LuisNowadays, society demands group decision making (GDM) problems that require the participation of a large number of experts, so-called large scale group decision making (LS-GDM) problems. Logically, the more experts are involved in the decision making process, the more common is the emergence of disagreements in the group. For this reason, consensus reaching processes (CRPs) are key in the resolution of these problems in order to smooth such disagreements in the group and reach consensual solutions. A CRP requires that experts are receptive to change their initial preferences, but demanding excessive changes could lead to deadlocks. The well-known minimum cost consensus (MCC) model allows to obtain an agreed solution by preserving experts’ preferences as much as possible. However, this MCC model only considers the distance among experts and collective opinion, which is not enough to guarantee a desired degree of consensus. To overcome this limitation, it was proposed comprehensive MCC models (CMCC) in which both consensus degree and distance are considered, and CMCC models deal with fuzzy preference relations (FPRs) for modeling experts’ opinions. However, these models are not efficient to deal with LS-GDM problems and the FPRs consistency is ignored in them. Therefore, this paper aims to propose new CMCC models focused on LS-GDM problems in which experts use FPRs whose consistency is taken into account in order to obtain reliable results. A case study is introduced to show the effectiveness of the proposed models.Ítem Explorando Nuevas Formulaciones de Ecuanimidad y Confiabilidad para Sistemas de Soporte a la Decisión Inteligentes(2025-01-24) Rodríguez-Domínguez, Rosa María; Dutta, Bapi; Martínez-López, Luis; Rodríguez-Domínguez, Rosa María; Dutta, Bapi; Labella, Álvaro; Yera, Raciel; García-Zamora, Diego; Sánchez-Sánchez, Pedro J.; Barranco, ManuelEl objetivo principal de este plan es garantizar que los datos generados en el proyecto FAIRANDTRUST cumplan con los principios FAIR (Fáciles de encontrar, Accesibles, Interoperables y Reutilizables). Este PGD busca facilitar el acceso, la interoperabilidad y la reutilización de los datos, asegurando al mismo tiempo su calidad y seguridad. Los objetivos específicos incluyen: • Facilitar el intercambio de datos entre la comunidad científica relacionada con los Sistemas de Soporte a la Decisión Inteligentes (IDSS). • Cumplir con los compromisos de publicación en acceso abierto establecidos por la entidad financiadora. • Garantizar un impacto duradero del proyecto más allá de su periodo de ejecución. • Promover la transparencia y confianza en los modelos de toma de decisiones inteligentes.