Decision making for renewable energy source selection using q-rung linear Diophantine fuzzy hypersoft aggregation operators

Selecting the optimal renewable energy source entails navigating through a complex decision-making landscape riddled with conflicting criteria and uncertain information. In this context, traditional approaches often fall short in adequately capturing the intricacies of such decision problems. This manuscript introduces a novel framework leveraging q-rung linear Diophantine fuzzy hypersoft sets as a practical tool for decision making in renewable energy source selection. The paper presents several aggregation operators tailored to q-rung linear Diophantine fuzzy hypersoft data, offering enhanced flexibility through adjustable operational parameters. By extending existing aggregation operators to this environment, we establish a robust multiple criteria decision-making method. Building upon these foundations, we develop amultiple criteria decision making (MCDM) technique for scenarios with unknown criterion weights within a q-rung linear Diophantine fuzzy hypersoft environment. An algorithm utilizing the proposed operators is provided to solve the renewable energy source selection problem effectively. We also explore the impact of parameter variations on decision outcomes and demonstrate the superiority of our techniques through comparative analysis with past approaches.