Scholarlink

<p>In this paper, we introduce a new decision-making algorithm based on the circular picture fuzzy heronian mean (C-PFHM) operator to assess sophisticated financial management policies under uncertainty. The method combines the advantages of picture fuzzy sets and heronian mean aggregation so that more sophisticated management of expert views with hesitation, indeterminacy, and vagueness is possible. Multi-criteria decision-making framework is developed with data collection, normalization, C-PFHM-based aggregation, defuzzification, and ranking. To test the approach, an actual financial case study is provided wherein several policy options are compared against major financial performance measures like return on investment, liquidity, and resistance to market volatility. Quantitative findings indicate that the new approach has a high correlation with reference methods resulting in a weighted Spearman’s rank correlation coefficient of 0.9815 when compared with C-PFHM-TOPSIS. This validates the performance and reliability of the proposed algorithm in high-fidelity decision-making environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Decision algorithm with fuzzy framework and evaluation of advanced financial management policy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月25日

查看全文
  • Zijie Yu,
  • Jiongchen Kou,
  • Yuanxin Liu,
  • Haoshan Chu

摘要

In this paper, we introduce a new decision-making algorithm based on the circular picture fuzzy heronian mean (C-PFHM) operator to assess sophisticated financial management policies under uncertainty. The method combines the advantages of picture fuzzy sets and heronian mean aggregation so that more sophisticated management of expert views with hesitation, indeterminacy, and vagueness is possible. Multi-criteria decision-making framework is developed with data collection, normalization, C-PFHM-based aggregation, defuzzification, and ranking. To test the approach, an actual financial case study is provided wherein several policy options are compared against major financial performance measures like return on investment, liquidity, and resistance to market volatility. Quantitative findings indicate that the new approach has a high correlation with reference methods resulting in a weighted Spearman’s rank correlation coefficient of 0.9815 when compared with C-PFHM-TOPSIS. This validates the performance and reliability of the proposed algorithm in high-fidelity decision-making environments.