An ORESTE approach based decision-making framework for renewable energy development with probabilistic dual hesitant fuzzy information

Development and utilization of renewable energy sources plays a crucial role in the sustainable development of a country. Selection among energy sources is a Multi-Criteria Decision-Making (MCDM) problem. Therefore, it is necessary to make an assessment in terms of several conflicting criteria. The aim of this paper is to develop the French Organization Rangement Et Synthese De Ronnees Relationnelles’ (ORESTE) approach for ranking the renewable energy plant under probabilistic dual hesitant fuzzy environment. First, an algorithm has been developed to normalize the Probabilistic Dual Hesitant Fuzzy Element (PDHFE). A series of new distance and similarity measures for PDHFEs under both discrete and continuous environments has also been developed in this article. To illustrate how well the developed method works, a sustainable renewable energy development problem has been taken to rank the renewable energy plant. To validate the working of the developed method a Sustainable Supplier Selection (SSS) problem has also been taken. To verify the robustness of proposed methodology, a comparative study and sensitive analysis has also been discussed.