Triangular Intuitionistic Fuzzy Frank Aggregation for Efficient Renewable Energy Project Selection

Abstract

Optimizing renewable energy project selection presents a complex challenge that demands intelligent decision-making under conditions of uncertainty. In multi-criteria decision-making (MCDM), the need to balance numerous conflicting factors makes these techniques invaluable for effective project evaluation and selection. This paper introduces a novel Triangular Intuitionistic Fuzzy Frank (TIFF) framework, which integrates triangular intuitionistic fuzzy averaging and geometric aggregation operators to enhance decision-making in renewable energy project assessment. We develop several new aggregation operators, including: Triangular Intuitionistic Fuzzy Frank Weighted Averaging (TIFFWA), Ordered Weighted Averaging (TIFFOWA), Hybrid Averaging (TIFFHA), Weighted Geometric (TIFFWG), Ordered Weighted Geometric (TIFFOWG), and Hybrid Geometric (TIFFHG). These operators, built upon the Frank t-norm and t-conorm, enable more accurate and adaptive evaluations by effectively managing
varying levels of uncertainty. In addition, novel scoring and precision functions are introduced to further refine the decision-making process, yielding more reliable outcomes. A step-by-step methodology is presented for applying the TIFF approach to renewable energy project selection, providing clear guidance for practical implementation. To validate the method, a numerical case study is conducted, demonstrating the superior performance of the TIFF framework compared to existing techniques. The results under-score the method’s efficiency, adaptability, and practical value as a robust tool for optimizing renewable energy project decisions under uncertainty.