As the generalization of intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS), the q-rung orthopair fuzzy set (q-ROFS) has emerged as a more meaningful and effective tool to solve multiple attribute group decision making (MAGDM) problems in management and scientific domains. The MABAC (multi-attributive border approximation area comparison) model, which handles the complex and uncertain decision making issues by computing the distance between each alternative and the bored approximation area (BAA), has been investigated by an increasing number of researchers more recent years. In our article, consider the conventional MABAC model and some fundamental theories of q-rung orthopair fuzzy set (q-ROFS), we shall introduce the q-rung orthopair fuzzy MABAC model to solve MADM problems. at first, we briefly review some basic theories related to q-ROFS and conventional MABAC model. Furthermore, the q-rung orthopair fuzzy MABAC model is built and the decision making steps are described. In the end, An actual MADM application has been given to testify this new model and some comparisons between this novel MABAC model and two q-ROFNs aggregation operators are provided to further demonstrate the merits of the q-rung orthopair fuzzy MABAC model.

随着直觉模糊集（IFS）和勾股模糊集（PFS）的推广，q阶邻对模糊集（q-ROFS）成为解决多属性组决策（MAGDM）问题的更有意义和有效的工具。在管理和科学领域。近年来，越来越多的研究人员对MABAC（多属性边界近似区域比较）模型进行了研究，该模型通过计算每个替代方案与钻孔近似区域（BAA）之间的距离来处理复杂且不确定的决策问题。 。在本文中，考虑常规MABAC模型和q-阶邻对模糊集（q-ROFS）的一些基本理论，我们将介绍q-阶邻对模糊MABAC模型来解决MADM问题。首先，我们简要回顾一些与q-ROFS和常规MABAC模型有关的基本理论。此外，建立了q-阶邻对模糊MABAC模型，并描述了决策步骤。最后，给出了一个实际的MADM应用来证明这一新模型，并对该新颖MABAC模型与两个q-ROFNs聚集算符进行了比较，以进一步证明q-阶邻对模糊MABAC模型的优点。