An extended multi-criteria decision-making technique for hydrogen and fuel cell supplier selection by using spherical fuzzy rough numbers

Fuel cells offer a clean energy alternative across various sectors, including transportation and power storage. Hydrogen fuel cells emit only water and heat, eliminating carbon dioxide and other harmful emissions. This makes them a sustainable choice, free from the costs and hazards associated with traditional fuels like diesel or battery acid. Overall, hydrogen fuel cells provide a zero-emission power source that supports environmental goals. With all the benefits of hydrogen fuel cells, selection of the most suitable hydrogen fuel cell is challenging due to the diverse range of technologies, performance metrics, and application requirements. Factors such as efficiency, cost, durability, and scalability must be carefully evaluated to meet specific needs effectively. Therefore, this research study develops a novel multi-criteria group decision making method to select the most apt hydrogen and fuel cell component supplier by considering socio-economic conditions of Iran. The proposed strategy combined with spherical fuzzy rough numbers makes the use of pairwise preferences to rank alternatives based on their performance across multiple criteria, aggregate them into an overall ranking and performs objective manipulation to reduce ambiguity to maximum level. The innovative idea of the spherical fuzzy rough number-based technique, Preference Ranking Organization Method for Enrichment of Evaluations, combines spherical fuzzy sets and rough numbers to enhance decision-making under uncertainty. This approach integrates the strengths of fuzzy logic and rough set theory to provide a more robust and flexible evaluation process in multi-criteria decision-making, allowing for better handling of ambiguity and incomplete information. This technique works by comparing alternatives based on multiple criteria through pairwise comparisons, ranking them according to preference functions. Its credibility is ensured by comparing it with already existing methods. At last, its potential implications and limitations are discussed.