A novel prioritized aggregation operator with trapezoidal hesitant intuitionistic fuzzy numbers and its applications to selection of an e-learning platform

In the digital age of education, e-learning has become an integral part of the learning process. However, decision-making processes in this field involve elements of uncertainty and hesitation that need to be managed effectively. This study addresses Multi-Criteria Decision Making (MADM) problems using Trapezoidal Hesitant Fuzzy Numbers (THIFN), a powerful tool for representing uncertain and hesitant information. Two new operators are proposed for situations where feature prioritization is required: THIFN-Prioritized Weighted Average (THIFNPWA) and THIFN-Prioritized Weighted Geometric (THIFNPWG). The fundamental properties of the proposed operators, such as uniformity, boundedness, and monotonicity, are theoretically examined. The developed approach is applied to a real case study for selecting the most suitable e-learning platform. A comprehensive comparative analysis is conducted with existing methods in the literature to prove the validity of the methodology. The analysis results showed that the proposed method accurately preserved priority relationships between features and produced more consistent and discriminatory ranking results compared to existing approaches. Findings from the comparative analysis demonstrate that this method exhibits superior performance in processing prioritized and ambiguous data and offers a reliable information fusion model for hesitant decision-making environments.