Optimal renewable energy source (RES) selection needs a strategic decision for reducing environmental pollutions, use of conventional resources, and improving economic development. In the process of RESs evaluation, several aspects like environmental, economic, social, and technical requirements play an important role. In addition, diverse factors affect the appropriate RES selection problem which adheres to uncertain and imprecise data. Thus, this selection process can be considered as a complex uncertain multi-criteria decision making (MCDM) problem. This study aims to introduce a novel integrated methodology based on Step-wise Weight Assessment Ratio Analysis (SWARA) and Combined Compromise Solution (CoCoSo) methods within single-valued neutrosophic sets (SVNSs) context, wherein the decision-makers and criteria weights are completely unknown. In the proposed approach, the criteria weights are determined by the SWARA method, and the most suitable RES alternative is determined by an improved CoCoSo method under the SVN context. Further, an illustrative case study of RES selection is considered to demonstrate the thorough execution process of the proposed method. Moreover, a comparison with existing methods is discussed to analyze the validity of the obtained result. This study performs sensitivity analysis with a various set of criteria weights to reveal the robustness of the developed approach. The strength of the proposed method is its practical applicability and ability to provide solutions under uncertain, imperfect, indeterminate, and inconsistent information.

The research introduces a credible decision-support tool for dealing with uncertainty for supplier selection in the aerated concrete industry. The developed framework includes criteria selection, determination of expert and criterion weights, and alternative ranking within a hyperbolic fuzzy environment. Criteria selection is done via the Laplacian score. Expert weights are methodically determined via entropy measure. Criteria are weighted using the LOPCOW and RANCOM methods, and alternatives are ranked using the hyperbolic extension of the DEPART method. The model is employed to solve a circular supplier selection problem in the aerated concrete industry. Comprehensive sensitivity and comparison checks are conducted.