Multi-criteria group decision-making problem under intuitionistic fuzzy logarithmic aggregation operators based on T-norm and T-conorm

The objective of this research is to present logarithmic operations for intuitionistic fuzzy sets (IF-Sets). Logarithmic operations can be used to reduce uncertainty in IF-Sets. For instance, taking the logarithm of the membership and non-membership degrees can help transform the values into a more interpretable scale, making it easier to assess the level of uncertainty. We introduce novel logarithmic aggregation operators under inducing variable, namely induced logarithmic intuitionistic fuzzy Einstein ordered weighted averaging aggregation (I-LIFEOWAA) operator, induced logarithmic intuitionistic fuzzy Einstein hybrid averaging aggregation (I-LIFEHAA) operator, induced logarithmic intuitionistic fuzzy Einstein ordered weighted geometric aggregation (I-LIFEOWGA) operator, induced logarithmic intuitionistic fuzzy Einstein hybrid geometric aggregation (I-LIFEHGA) operator. Inducing aggregation operators are versatile tools that contribute to more robust decision-making and assessment processes by effectively combining diverse sources of information. Their applications span across different industries and domains where complex decisions involve the integration of multiple criteria. These operators are used to aggregate different pieces of information or criteria into a single value, providing a comprehensive assessment. At the end of the paper, we develop an algorithm for decision-making process. An illustrative example related to the selection of more suitable vaccine for COVID-19 patients in Pakistan is to be considered to show the effectiveness and efficiency of the novel techniques.