Some intuitionistic linguistic dependent Bonferroni mean operators and application in group decision-making

The dependent ordered weighted averaging (DOWA) operator can relieve the influence of unfair data from the aggregated arguments, and Bonferroni mean (BM) operator can capture the interrelationship of the aggregated arguments. In order to making fully use of the advantages of these two types of operators, we combine the DOWA with the BM operator in intuitionistic linguistic setting, and propose the intuitionistic linguistic dependent Bonferroni mean (ILDBM) operator and the intuitionistic linguistic dependent geometric Bonferroni mean (ILDGBM) operator. Simultaneously, several properties of these novel operators are discussed. Moreover, a method based on these operators is developed to solve the multi-attribute group decision making(MAGDM) problems with intuitionistic linguistic information. The advantages of the proposed method are (1) it can consider the interrelationship between any two attribute values; (2) it can relieve the influence of unfair attribute values given by some biased decision makers. Finally, an application example is represented to illustrate the practicality and validity of the developed method by comparing with the existing methods.