The compromise solution of the multi-criteria decision-making (MCDM) problem by the existing VIKOR method for Pythagorean fuzzy sets (PyFSs) is not closest to the positive ideal solution. This is because the defining function for VIKOR does not obey the axioms for a dissimilarity measure. Thus in this context, the existing notion of dissimilarity measures and the VIKOR method are controversial. This study aims to provide a new dissimilarity measure and refine the VIKOR method for PyFSs accordingly. We define a new dissimilarity measure for PyFSs and refine the existing VIKOR method. We discuss the additional properties of dissimilarity measures and improve the ideas of remoteness and ranking indexes. We provide numerical examples to support the analysis and findings of our study. Finally, we solve the MCDM problems to illustrate the proposed method.

中文翻译：

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改进的基于广义相异测度的毕达哥拉斯模糊集VIKOR方法

现有的毕达哥拉斯模糊集（PyFSs）VIKOR方法对多准则决策（MCDM）问题的折衷解并不最接近正理想解。这是因为 VIKOR 的定义函数不遵守相异性度量的公理。因此，在这种情况下，现有的差异度量概念和 VIKOR 方法是有争议的。本研究旨在提供一种新的差异度量并相应地改进 PyFS 的 VIKOR 方法。我们为 PyFS 定义了一个新的差异度量并改进了现有的 VIKOR 方法。我们讨论了差异度量的附加属性，并改进了远程和排名指数的想法。我们提供了数值例子来支持我们研究的分析和发现。最后，