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<p>The identification of competitive renewable energy (RE) sources often fails to account for non-technical factors, such as environmental, political, and social barriers. These considerations of RE are described in language that accommodates varying levels of hesitancy among decision-makers (DMs). This study develops a multi-criteria group decision-making (MCGDM) method using hesitant bi-fuzzy sets to capture DMs’ varying degrees of hesitancy. A HBF extension of multi-objective optimization on the basis of ratio analysis is proposed, incorporating a method based on the removal effect on the criteria to assign parameter importance for each DM. This study also developed a novel HBF-Dombi aggregating operator for logical synthesis of qualitative data. The developed MCGDM framework is applied to determine the optimal RE sources, considering 5 DMs, 6 RE sources, and 15 barriers classified into 5 categories. Solar energy emerges as the top choice since it is favorable in both technical and non-technical RE barriers. A comparative analysis is performed to validate the proposed method, while comprehensive sensitivity analyses are performed to assess the influence of parameter variation on the ranking of RE sources. The insights from this article can serve as a pathway for identifying RE barriers to successful RE installation.</p>

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Robust ranking of renewable energy alternatives handling uncertainty using novel hesitant bi-fuzzy MEREC-MOORA and Dombi aggregation approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Bahnisikha Roy Muhuri,
  • B. S. Mahapatra,
  • G. S. Mahapatra,
  • Debashis Ghosh,
  • Prabhjot Kaur,
  • Arti Choudhary

摘要

The identification of competitive renewable energy (RE) sources often fails to account for non-technical factors, such as environmental, political, and social barriers. These considerations of RE are described in language that accommodates varying levels of hesitancy among decision-makers (DMs). This study develops a multi-criteria group decision-making (MCGDM) method using hesitant bi-fuzzy sets to capture DMs’ varying degrees of hesitancy. A HBF extension of multi-objective optimization on the basis of ratio analysis is proposed, incorporating a method based on the removal effect on the criteria to assign parameter importance for each DM. This study also developed a novel HBF-Dombi aggregating operator for logical synthesis of qualitative data. The developed MCGDM framework is applied to determine the optimal RE sources, considering 5 DMs, 6 RE sources, and 15 barriers classified into 5 categories. Solar energy emerges as the top choice since it is favorable in both technical and non-technical RE barriers. A comparative analysis is performed to validate the proposed method, while comprehensive sensitivity analyses are performed to assess the influence of parameter variation on the ranking of RE sources. The insights from this article can serve as a pathway for identifying RE barriers to successful RE installation.