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In an information age, people often need to face a lot of decision-making information when making decisions. Some indicators are on the high side and others are on the low side which is a common phenomenon in decision-making. So, it is difficult to make a correct and rational judgment. Long-term research has proved that information aggregation operator is an effective tool to solve this kind of problem. Bonferroni mean (BM) is an important information aggregation tool which has the main feature of capturing the interrelationships among aggregated arguments. Because the existing geometric Bonferroni mean (GBM) cannot reflect the two-layer average calculation and the weighted GBM do not feature reducibility, this paper develops the intuitionistic fuzzy normalized weighted optimized GBM (IFNWOGBM) and the generalized intuitionistic fuzzy normalized weighted optimized GBM (GIFNWOGBM) and also studies their desirable properties and special cases. In the end, based on the IFNWOGBM and GIFNWOGBM, a method to multiple attribute decision-making (MADM) problem is proposed. In order to verify the effectiveness of the method, it is used to select the location of the library.

Generalized Intuitionistic Fuzzy Normalized Weighted Optimized Geometric Bonferroni Mean and Their Application to MADM