Soft set (SfS) theory is a basic tool to handle vague information with parameterized study during the process as compared to fuzzy as well as q-rung orthopair fuzzy theory. This research article is devoted to establish some general aggregation operators (AOs), based on Yager’s norm operations, to cumulate the q-rung orthopair fuzzy soft data in decision making environments. In this article, the valuable properties of q-rung orthopair fuzzy soft set (q - ROFSfS) are merged with the Yager operator to propose four new operators, namely, q-rung orthopair fuzzy soft Yager weighted average (q - ROFSfYWA), q-rung orthopair fuzzy soft Yager ordered weighted average (q - ROFSfYOWA), q-rung orthopair fuzzy soft Yager weighted geometric (q - ROFSfYWG) and q-rung orthopair fuzzy soft Yager ordered weighted geometric (q - ROFSfYOWG) operators. The dominant properties of proposed operators are elaborated. To emphasize the importance of proposed operators, a multi-attribute group decision making (MAGDM) strategy is presented along with an application in medical diagnosis. The comparative study shows superiorities of the proposed operators and limitations of the existing operators. The comparison with Pythagorean fuzzy TOPSIS (PF-TOSIS) method shows that PF-TOPSIS method cannot deal with data involving parametric study but developed operators have the ability to deal with decision making problems using parameterized information.

中文翻译：

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基于q-阶邻态对模糊软信息的混合决策方法

与模糊和q阶邻对模糊理论相比，软集（SfS）理论是在处理过程中通过参数化研究处理模糊信息的基本工具。这篇研究文章致力于基于Yager的范数运算建立一些通用的聚合算子（AO），以在决策环境中累积q-阶邻对模糊软数据。在本文中，将q阶邻对模糊软集合（q-ROFSfS）的有价值的属性与Yager运算符合并，以提出四个新的运算符，即q阶邻对模糊软Yager加权平均值（q-ROFSfYWA），q -阶邻对模糊软Yager有序加权平均值（q-ROFSfYOWG），q-阶邻对模糊软Yager有序几何（q-ROFSfYWG）和q-阶邻对模糊软Yager有序加权几何（q-ROFSfYOWG）运算符。阐述了所提议的算子的主要属性。为了强调提议的操作员的重要性，提出了一种多属性小组决策（MAGDM）策略及其在医学诊断中的应用。对比研究显示了建议的运营商的优势和现有运营商的局限性。与毕达哥拉斯模糊TOPSIS（PF-TOSIS）方法的比较表明，PF-TOPSIS方法无法处理涉及参数研究的数据，但发达的算子可以使用参数化信息处理决策问题。对比研究显示了建议的运营商的优势和现有运营商的局限性。与毕达哥拉斯模糊TOPSIS（PF-TOSIS）方法的比较表明，PF-TOPSIS方法无法处理涉及参数研究的数据，但发达的算子可以使用参数化信息处理决策问题。对比研究显示了建议的运营商的优势和现有运营商的局限性。与毕达哥拉斯模糊TOPSIS（PF-TOSIS）方法的比较表明，PF-TOPSIS方法无法处理涉及参数研究的数据，但发达的算子可以使用参数化信息处理决策问题。