Covering based multigranulation (I, T)-fuzzy rough set models and applications in multi-attribute group decision-making

By means of a fuzzy logical implicator and a t-norm (respectively denoted I and T), we introduce covering based multigranulation (I, T)-fuzzy rough set models from fuzzy beta-neighborhoods. By using different implicators and t-norms, the corresponding axiomatic characterizations of covering based optimistic, pessimistic and variable precision multi granulation (I, T)-fuzzy rough set models are investigated. Connections among these kinds of coverings based models are examined. Based on the theoretical analysis for the covering based multigranulation (I, T)-fuzzy rough set models, solutions to problems in multi-attribute group decision-making by means of two kinds of decision-making methods are respectively established. An effective example is fully developed to illustrate these methodologies. Comparative analysis shows that the two ranking results obtained by means of two different decision-making methods have a high consensus. (C) 2018 Elsevier Inc. All rights reserved.