Information Measures and Aggregation Operators on Fuzzy / Intuitionistic Fuzzy Sets with Applications in Decision Making

In nature as well as otherwise there are what we can call deterministic and non-deterministic phenomena. Non-deterministic ones attract more challenges in scientific study. When the patterns of occurrence of non-deterministic phenomena are stable in their occurrence, their study was taken up in statistics. However, in real world, there is a large variety of vague phenomena, imprecise concepts and not well defined quantities, which fall beyond the scope of conventional precise mathematics. For such vague phenomena, the term 'Fuzzy' has been coined in mathematics. Systematically, the theory of fuzzy sets (FSs) was introduced by Zadeh [48] in 1965, as a generalization of classical set theory, for representing imprecise and vague phenomena. Fuzzy set theory allows objects to be members of the set with a degree of membership which can take any value in the closed interval [ ] 1 , 0. Zadeh [48] has proposed the following definition of a fuzzy set: A fuzzy set A ~ defined in a discrete universe of discourse