Evaluating renewable energy projects using fuzzy bipolar soft aggregation and entropy weights

The fuzzy bipolar soft set (FBPSS) introduces a novel approach that surpasses the information capacity of conventional fuzzy soft set. The core aim of FBPSS is to simultaneously incorporate two weight vectors, one for attributes and another for parameters. This dual vector consideration enhances precision for decision-makers. In this research, we leverage the Frank t-norm and t-conorm to introduce a set of aggregation operators within the framework of FBPSS. We thoroughly delve into the essential properties of these aggregation operators. Using these operators as a foundation, we construct a method for multi-criteria group decision-making (MCGDM). To exemplify the efficacy and efficiency of our proposed approach, we provide a practical case study involving the assessment of a renewable energy project. Additionally, we explore the Frank parameters of the decision-making process. To validate our method, we compare its results with those obtained from existing approaches. Lastly, we engage in a comprehensive discussion concerning both the merits and limitations of our proposed approach.