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<p>This paper explores an enhanced strategy to address the limitations of current combinative distance-based assessment (CODAS) methods, which often overlook the interrelationships between attributes. The proposed method uses the Heronian Minkowski distance aggregation operator to tackle complex fermatean fuzzy (CFF) multiple criteria group decision-making (MCGDM) problems when there is total uncertainty regarding the information about weight. Initially, the paper develops CFF weighted Heronian Minkowski distance (CFFWHMD) aggregation operators with a broader application, building on the Heronian mean (HM) and Minkowski-type distance (MTD). The paper then outlines the CFF MCGDM methodology using the improved CODAS approach. Here, the MTD distance is employed to determine expert weights and create the maximizing deviation method (MDM) for attribute weight determination. The CODAS method incorporates CFF ordered weighted Heronian Hamming distance (CFFOWHHD) and CFF ordered weighted Heronian Euclidean (CFFOWHED) operators extracted from the CFFOWHMD operator. This enhancement enables a more effective assessment of the deviation between alternative solutions and better captures attribute correlations. Finally, the paper illustrates the applicability and viability of this methodology using an example involving the selection of an investment idea. The study also includes sensitivity evaluation and comparative study to illustrate the stability and accuracy of the suggested technique.</p>

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CODAS with heronian minkowski distance operator: a novel approach for complex fermatean fuzzy multi-criteria group decision analysis

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Madiha Ghamkhar,
  • Fakiha Ijaz,
  • Jawad Ali,
  • Muhammad Azeem

摘要

This paper explores an enhanced strategy to address the limitations of current combinative distance-based assessment (CODAS) methods, which often overlook the interrelationships between attributes. The proposed method uses the Heronian Minkowski distance aggregation operator to tackle complex fermatean fuzzy (CFF) multiple criteria group decision-making (MCGDM) problems when there is total uncertainty regarding the information about weight. Initially, the paper develops CFF weighted Heronian Minkowski distance (CFFWHMD) aggregation operators with a broader application, building on the Heronian mean (HM) and Minkowski-type distance (MTD). The paper then outlines the CFF MCGDM methodology using the improved CODAS approach. Here, the MTD distance is employed to determine expert weights and create the maximizing deviation method (MDM) for attribute weight determination. The CODAS method incorporates CFF ordered weighted Heronian Hamming distance (CFFOWHHD) and CFF ordered weighted Heronian Euclidean (CFFOWHED) operators extracted from the CFFOWHMD operator. This enhancement enables a more effective assessment of the deviation between alternative solutions and better captures attribute correlations. Finally, the paper illustrates the applicability and viability of this methodology using an example involving the selection of an investment idea. The study also includes sensitivity evaluation and comparative study to illustrate the stability and accuracy of the suggested technique.