A Cutting Edge Algorithm for Interval-Valued Intuitionistic Fuzzy Decision Making Based on Mean, Variance of Alternative Score Matrices and A New Score Function

The interval-valued intuitionistic fuzzy (IVIF) set (IVIFS) is a powerful and valuable medium for expressing decision makers (DMs) thoughts and handling imprecise information. The present research is mainly partitioned into two portions in order to create a better decision making pattern. Initially, a generalized score function (GSF) for IVIF values (IVIFVs) is created, and its quality features are covered. This GSF is not only easy to apply but also conquers the weakness of the past score functions. Thereafter, in the IVIF context, the solution steps for a new algorithm have been built up for the multiple attribute decision making (MADM) problems. In it, the attribute weights and values given by the DM are conveyed in the form of IVIFVs. On the basis of mean and variance of each alternative, a standard value is calculated for the final ranking of alternatives. The best alternative comes out corresponding to a larger standard value. Various numerical examples and comparisons with existing methods are also given to show the validity, applicability, and superiority of the present research