Applied Decision Analytics

Vol. 2 No. 1 (2026): Applied Decision Analytics
Articles

An MCDM-based Optimization Approach for Identifying Efficient Renewable Energy Sources under Fermatean Fuzzy Environment

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  • Prodip Bhaduri,
  • Aditi Biswas,
  • Kamal Hossain Gazi,
  • Sankar Prasad Mondal
Prodip Bhaduri
Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal
Aditi Biswas
Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal
Kamal Hossain Gazi
Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal
Sankar Prasad Mondal
Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal

Published 2026-05-21

Keywords

  • Renewable energy source,
  • Efficient renewable energy source,
  • Fermatean fuzzy number (FFN),
  • Optimization,
  • DEMATEL

Copyright (c) 2026 Prodip Bhaduri, Aditi Biswas, Kamal Hossain Gazi, Sankar Prasad Mondal (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Bhaduri, P., Biswas, A., Gazi, K. H., & Mondal, S. P. (2026). An MCDM-based Optimization Approach for Identifying Efficient Renewable Energy Sources under Fermatean Fuzzy Environment. Applied Decision Analytics, 2(1), 320-351. https://doi.org/10.66972/ada21202627
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Abstract

In recent decades, the growing global demand for energy and the depletion of conventional fossil fuel resources have intensified the need for sustainable and renewable energy alternatives. Consequently, identifying efficient renewable energy sources has become a critical challenge for policymakers and energy planners worldwide. This study evaluates major renewable energy sources, including solar, wind, tidal, hydropower, geothermal, and biomass energy, with the aim of ranking them according to their technical efficiency. To achieve this objective, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is applied within a Fermatean fuzzy framework to address uncertainty and ambiguity in expert judgments. Furthermore, a comparative analysis under different uncertainty scenarios is conducted to validate the robustness and reliability of the obtained results. The proposed approach provides a systematic decision-support framework for researchers, energy-sector authorities, and policymakers to facilitate sustainable and informed energy planning

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