Developing dynamic intuitionistic normal fuzzy aggregation operators for multi-attribute decision-making with time sequence preference

In allusion to dynamic intuitionistic normal fuzzy multi-attribute decision-making (MADM) problems with unknown time weight, a MADM method based on dynamic intuitionistic normal fuzzy aggregation (DINFA) operators and VIKOR method with time sequence preference was presented. In this method, two information aggregating operators were first proposed and proved, including dynamic intuitionistic normal fuzzy weighted arithmetic average (DINFWAA) operator and dynamic intuitionistic normal fuzzy weighted geometric average (DINFWGA) operator. Meanwhile, we constructed a multi-target nonlinear programming model, which fused time degree theory that was based on subjective preference and information entropy principle based on objective preference, to obtain time weight. Based on which, according to the algorithm of intuitionistic normal fuzzy number, intuitionistic normal fuzzy information under different time sequences were aggregated by using the proposed DINFA operators, and formed a dynamic intuitionistic normal fuzzy comprehensive decision-making matrix; then, obtained the optimal solution that was the closest to ideal solution via VIKOR method. Finally, the feasibility and significance of the presented method over existing methods were verified via analysis of numerical examples.