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Strategic MARCOS Model for Optimizing Renewable Energy Investments Under Pythagorean Hesitant Fuzzy Assessments

-д хадгалсан:
-д хэвлэсэн:Advances in Fuzzy Systems vol. 2025 (2025)
Үндсэн зохиолч: Younis, Muhammad
Бусад зохиолчид: Shahzaib Ashraf, Abdullah, Saleem, Tooba Shahid, Gokul, K C
Хэвлэсэн:
John Wiley & Sons, Inc.
Нөхцлүүд:
Methodology
Fuzzy sets
Artificial intelligence
Sustainable development
Multiple criterion
Decision making
Environmental stewardship
Renewable resources
Optimization
Algorithms
Renewable energy
Alternative energy sources
Energy resources
Climate change
Cost effectiveness
Investments
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Хураангуй:For sustainable growth, investments in renewable energy must be maximized. Maximizing investments in renewable energy opens the door to a more successful and environmentally friendly future. Analyzing technological viability, cost-effectiveness, regulatory compliance, and environmental impact are all part of this optimization process. This paper delves into a sophisticated methodology designed to tackle uncertainties in decision-making by leveraging the innovative concept of Pythagorean hesitant fuzzy sets (PHFSs). We defined aggregation operations and distance measures for PHFS. After that, we introduced Measurement of Alternatives and Ranking According to the Compromise Solution (MARCOS), a novel methodology under PHFS, it is a robust tool acknowledged for navigating complex decision scenarios with multiple criteria. Following that, we showcased a case study on enhancing renewable energy investments through an AI-based strategy for sustainable development, utilizing the newly developed MARCOS algorithm. The study highlights the significance of its adaptability and efficiency in practical applications. Furthermore, we compared this methodology with the Technique for Establishing Order Performance by Similarity to the Ideal Solution (TOPSIS), offering insights into their respective strengths. This offers a concrete demonstration of its real-world utility and potential impact in decision-making scenarios. Finally, in the last, we conclude the whole study.
ISSN:1687-7101
1687-711X
DOI:10.1155/adfs/6193403
Эх сурвалж:Advanced Technologies & Aerospace Database