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Pythagorean Fuzzy Partitioned Geometric Bonferroni Mean and Its Application to Multi-criteria Group Decision Making with Grey Relational Analysis

机译:勾股数模糊划分几何Bonferroni均值及其在灰色关联分析的多准则群决策中的应用

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摘要

Based on the partitioned structure of the set of criteria, partitioned geometric Bonferroni mean (PGBM) availably takes into account the heterogeneous relationships between the criteria in the information aggregation of multi-criteria decision making, which can describe the internal relationship and external relationship pattern among criteria. Inspired by the idea, we introduce PGBM into the Pythagorean fuzzy environment and propose two new aggregation operators, i.e., Pythagorean fuzzy partitioned geometric Bonferroni mean and weighted Pythagorean fuzzy partitioned geometric Bonferroni mean (WPFPGBM). Meanwhile, we also examine some special cases and properties of these operators. Then, we deeply investigate the application of WPFPGBM in the Pythagorean fuzzy multi-criteria group decision-making (PFMCGDM) problem. Firstly, we employ theWPFPGBM operator to integrate the Pythagorean fuzzy information for each decision maker. Furthermore, with the help of the grey relational analysis, we design an optimization model to determine the weights of the decision makers and further propose a method for the application of PFMCGDM, i.e., obtain the relative relational degree of the alternatives and rank them accordingly. Finally, the assessment of commercial banks' credit risk in Ghana is used to illustrate and verify our proposed method.
机译:基于准则集的划分结构,可划分的几何Bonferroni均值(PGBM)在多准则决策信息聚合中有效地考虑了准则之间的异类关系,可以描述准则之间的内部关系和外部关系模式标准。受此想法启发,我们将PGBM引入毕达哥拉斯模糊环境,并提出了两个新的聚合算子,即毕达哥拉斯模糊分区几何Bonferroni均值和加权毕达哥拉斯模糊分区几何Bonferroni均值(WPFPGBM)。同时,我们还研究了这些运算符的一些特殊情况和属性。然后,我们深入研究了WPFPGBM在勾股模糊多准则群决策(PFMCGDM)问题中的应用。首先,我们使用WPFPGBM运算符为每个决策者集成勾股模糊信息。此外,在灰色关联分析的帮助下,我们设计了一个优化模型来确定决策者的权重,并进一步提出了应用PFMCGDM的方法,即获得备选方案的相对关系度并对其进行相应排序。最后,通过对加纳商业银行信用风险的评估来说明和验证我们提出的方法。

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