Hesitant fuzzy linguistic aggregation operators based on global vision

The hesitant fuzzy linguistic term set is an efficient tool for modeling quantitative information. In recent years, hesitant fuzzy linguistic information aggregation operators have increasingly attracted the attention of scholars. However, most of the existing operators assume that aggregated elements are independent, which overlooks the interconnectedness between elements in decision making situations. In this paper, we introduce several tools for integrating hesitant fuzzy linguistic variables, such as the hesitant fuzzy linguistic reducible weighted Bonferroni mean (HFLRWBM) operator, the hesitant fuzzy linguistic generalized reducible weighted Bonferroni mean (HFLGRWBM) operator, and the hesitant fuzzy linguistic weighted power Bonferroni mean (HFLWPBM) operator. These operators take into consideration the influences of two factors: the relative importance of each individual criterion and the interaction relationships between the values. It should be noted that the HFLWPBM operator is suitable for dealing with hesitant fuzzy linguistic evaluated information provided by decision makers are fused. In addition, several special forms of these operators are investigated, and their properties and advantages are discussed. Using these operators, this paper proposed a method for multiple attribute decision making. The feasibility and validity of this approach are demonstrated using a number of examples.