A Novel Dynamic Intuitionistic Fuzzy MADM Approach

Abstract

This paper explores the multiple attribute decision making problems with dynamic intuitionistic fuzzy information. The notion of intuitionistic fuzzy variable is defined, and one new aggregation operator: dynamic dependent intuitionistic fuzzy Einstein weighted average (DDIFEWA) operator is presented. And based on the D-DIFEWA operator, we develop a procedure to solve the dynamic intuitionistic fuzzy multi-attribute decision making (DIF-MADM) problems where all the decision information about attribute values takes the form of intuitionistic fuzzy numbers and is collected at different periods. Finally, a numerical example is presented to show the applicability of the proposed method for the advanced mathematics teaching effectiveness problem and a sensitivity analysis is conducted to demonstrate efficiency of dynamic evaluation.