Abstract

AbstractIn Group Decision Making (GDM) the automatic consensus models are guided by different consensus measures which usually are obtained by aggregating similarities observed among experts’ opinions. Most GDM problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts’ opinions.However, there exist problemswhose assessments need to be represented by means of unbalanced linguistic term sets, i.e., using term sets which are not uniformly and symmetrically distributed. The aim of this paper is to present different Linguistic OWA Operators to compute the consensus measures in consensusmodels for GDMproblems with unbalanced fuzzy linguistic information.KeywordsGroup DecisionLinguistic TermLinguistic InformationConsensus ModelProximity MeasureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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