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<p>Decision-making in uncertain and complex environments requires methods that can effectively capture ambiguity in expert judgments. Traditional fuzzy graph models often overlook non-membership and hesitation degrees, limiting their descriptive power. To address this limitation, we propose a novel group decision-making framework based on intuitionistic fuzzy graphs (IFG’s), integrating Signless Laplacian energy (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(S_{LE}\)</EquationSource> </InlineEquation>) with a correlation coefficient (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(C_c\)</EquationSource> </InlineEquation>) measure. In the proposed model, <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(S_{LE}\)</EquationSource> </InlineEquation> is used to derive objective criterion weights, while <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(C_c\)</EquationSource> </InlineEquation> evaluates the similarity among alternatives. Two complementary procedures ensure consistent and robust rankings. The framework is validated through a case study on electric vehicle selection, considering alternatives based on range, safety, and price. Results show that both procedures produce identical rankings and improve decision consistency compared with existing methods using only <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(S_{LE}\)</EquationSource> </InlineEquation> or <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(C_c\)</EquationSource> </InlineEquation>. This demonstrates that the unified framework reduces ambiguity, enhances ranking reliability, and provides practical support for sustainable technology adoption under uncertainty.</p>

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Intuitionistic fuzzy approach based on correlation coefficient and signless Laplacian energy with applications

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月27日

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  • A. Mohamed Atheeque,
  • S. Sharief Basha

摘要

Decision-making in uncertain and complex environments requires methods that can effectively capture ambiguity in expert judgments. Traditional fuzzy graph models often overlook non-membership and hesitation degrees, limiting their descriptive power. To address this limitation, we propose a novel group decision-making framework based on intuitionistic fuzzy graphs (IFG’s), integrating Signless Laplacian energy ( \(S_{LE}\) ) with a correlation coefficient ( \(C_c\) ) measure. In the proposed model, \(S_{LE}\) is used to derive objective criterion weights, while \(C_c\) evaluates the similarity among alternatives. Two complementary procedures ensure consistent and robust rankings. The framework is validated through a case study on electric vehicle selection, considering alternatives based on range, safety, and price. Results show that both procedures produce identical rankings and improve decision consistency compared with existing methods using only \(S_{LE}\) or \(C_c\) . This demonstrates that the unified framework reduces ambiguity, enhances ranking reliability, and provides practical support for sustainable technology adoption under uncertainty.