Optimizing decision-making in electric power system selection: A generalized approach based on Hamacher aggregation operators for q-rung orthopair fuzzy soft sets

Identifying the most suitable power source that is cost-effective, environmentally friendly, socially accepted, and capable of ensuring long-term sustainability represents a critical decision-making (DM) problem. Existing models based on traditional fuzzy sets and specific norms cannot describe complex issues such as selecting the best electric power system (EPS). These decision models can also be challenging, especially when transparency and regulatory compliance are required. This study introduces a novel operational framework based on Hamacher aggregation operators for q-rung orthopair fuzzy set soft sets. It aims to help decision-makers identify appropriate policies to overcome the shortfall of electric power. The advantages of the introduced decision support tool are better discrimination ability, well suited for handling extreme values in data, greater flexibility in representing complex DM scenarios, and parametrization. We also demonstrate the practical application of our approach to a real-world problem related to selecting the most suitable EPS in Pakistan. The selection is made from the set of five EPSs and is based on critical aspects like social acceptance, environmental impact, and cost-effectiveness. The findings show that the developed approach effectively identifies the best EPS. Tuning variables like q and ℓ allows for testing the sensitivity of the developed operators. The results showed that higher values of q make the uncertainty more evenly represented, which is essential for specific uses. The comparison with the existing approaches suggests the superiority of the proposed analytic framework.