On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Group decision making and consensus under fuzzy preferences and fuzzy majority
Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
A model of consensus in group decision making under linguistic assessments
Fuzzy Sets and Systems
Deriving consensus in multiagent systems
Artificial Intelligence
A rational consensus model in group decision making using linguistic assessments
Fuzzy Sets and Systems
Decision support systems in the twenty-first century
Decision support systems in the twenty-first century
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy Multiple Attribute Decision Making: Methods and Applications
International Journal of Intelligent Systems
Group Decision-Making Model With Incomplete Fuzzy Preference Relations Based on Additive Consistency
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A linguistic modeling of consensus in group decision making basedon OWA operators
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A consensus model for multiperson decision making with different preference structures
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
A Consensus Model for Group Decision Making With Incomplete Fuzzy Preference Relations
IEEE Transactions on Fuzzy Systems
A Fuzzy Linguistic Methodology to Deal With Unbalanced Linguistic Term Sets
IEEE Transactions on Fuzzy Systems
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In group decision making problems, a natural question in the consensus process is how to measure the closeness among experts' opinions in order to obtain the consensus level. To do so, different approaches have been proposed. For instance, several authors have introduced hard consensus measures varying between 0 (no consensus or partial consensus) and 1 (full consensus or complete agreement). However, consensus as a full and unanimous agreement is far from being achieved in real situations. So, in practice, a more realistic approach is to use softer consensus measures, which assess the consensus degree in a more flexible way. The aim of this paper is to identify the different existing approaches to compute soft consensus measures in fuzzy group decision making problems. Additionally, we analyze their advantages and drawbacks and study the future trends.