A Granular Computing-Based Model for Group Decision-Making in Multi-Criteria and Heterogeneous Environments

dc.contributor.authorCabrerizo, Francisco
dc.contributor.authorGonzález-Quesada, Juan Carlos
dc.contributor.authorPérez, Ignacio
dc.contributor.authorHerrera-Viedma, Enrique
dc.date.accessioned2021-12-24T17:36:31Z
dc.date.available2021-12-24T17:36:31Z
dc.date.issued2022-01-04
dc.description.abstractGranular computing is a growing computing paradigm of information processing that covers any techniques, methodologies, and theories employing information granules in complex problem solving. Within the recent past, it has been applied to solve group decision-making processes and different granular computing-based models have been constructed, which focus on some particular aspects of these decision-making processes. This study presents a new granular computing-based model for group decision-making processes defined in multi-criteria and heterogeneous environments that is able to improve with minimum adjustment both the consistency associated with individual decision-makers and the consensus related to the group. Unlike the existing granular computing-based approaches, this new one is able to take into account a higher number of features when dealing with this kind of decision-making processes.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.262
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79594
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSoft Computing: Theory Innovations and Problem Solving Benefits
dc.subjectconsensus
dc.subjectconsistency
dc.subjectdecision-making
dc.subjectgranular computing
dc.subjectminimum adjustment
dc.titleA Granular Computing-Based Model for Group Decision-Making in Multi-Criteria and Heterogeneous Environments
dc.type.dcmitext

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