Renewable Energy Sources Selection Analysis with the Maximizing Deviation Method
This addresses the selection of renewable energy sources for decision-makers, but it appears incremental as it builds on existing fuzzy set and multi-criteria decision-making methods.
The study tackled the problem of selecting renewable energy sources by proposing an optimization model based on the deviation maximization method combined with interval-valued Fermatean fuzzy sets to handle uncertainty in decision-making, but no concrete results or numbers were provided in the abstract.
Multi-criteria decision-making methods provide decision-makers with appropriate tools to make better decisions in uncertain, complex, and conflicting situations. Fuzzy set theory primarily deals with the uncertainty inherent in human thoughts and perceptions and attempts to quantify this uncertainty. Fuzzy logic and fuzzy set theory are utilized with multi-criteria decision-making methods because they effectively handle uncertainty and fuzziness in decision-makers' judgments, allowing for verbal judgments of the problem. This study utilizes the Fermatean fuzzy environment, a generalization of fuzzy sets. An optimization model based on the deviation maximization method is proposed to determine partially known feature weights. This method is combined with interval-valued Fermatean fuzzy sets. The proposed method was applied to the problem of selecting renewable energy sources. The reason for choosing renewable energy sources is that meeting energy needs from renewable sources, balancing carbon emissions, and mitigating the effects of global climate change are among the most critical issues of the recent period. Even though selecting renewable energy sources is a technical issue, the managerial and political implications of this issue are also important, and are discussed in this study.