Bounded rationality of decision-makers and interaction among criteria plays a key role in the actual multi-criteria group decision-making (MCGDM) process. This paper aims to offer a decision approach to a MCGDM problem under hesitant fuzzy linguistic (HFL) uncertainty, by means of behavioral multigranulation decision-theoretic rough set (DTRS), where criteria interaction is handled by Choquet integral, and decision-makers’ psychological preferences are considered by prospect theory. In specific, we firstly define the behavioral DTRS over two universes, where the threshold parameters are obtained by maximizing expected utility, rather than minimizing expected risk. Then, from the perspective of the difference of alternatives, we develop the calculation methods of loss and benefit functions, and provide the determination of the overall threshold parameters by Choquet integral, in consideration of criteria interaction. With these discussions, the behavioral multigranulation DTRS over two universes in the HFL context is proposed, and the two special cases, i.e., optimistic and pessimistic versions are also discussed. Thus, solutions to a MCGDM issue, green supplier selection, by utilizing the proposed behavioral multigranulation HFL DTRS over two universes, are designed, along with key steps and algorithm. Ultimately, an illustrative example is given to elaborate the practicability and feasibility of our presented model.

决策者的无限理性和标准之间的相互作用在实际的多标准小组决策（MCGDM）过程中起着关键作用。本文旨在通过行为多粒度决策理论粗糙集（DTRS）为犹豫的模糊语言（HFL）不确定性下的MCGDM问题提供决策方法，其中标准交互作用由Choquet积分处理，决策者的心理前景理论考虑了偏好。具体来说，我们首先定义两个宇宙上的行为DTRS，其中阈值参数是通过最大化预期效用而不是最小化预期风险来获得的。然后，从替代方案的差异角度出发，我们开发了损失和收益函数的计算方法，并考虑标准交互作用，通过Choquet积分确定总体阈值参数。通过这些讨论，提出了在HFL上下文中两个宇宙上的行为多粒度DTRS，并讨论了乐观和悲观两种特殊情况。因此，设计了MCGDM问题（绿色供应商选择）的解决方案，方法是在两个范围内利用所提议的行为多粒度HFL DTRS，以及关键步骤和算法。最终，给出一个说明性的例子来阐述我们提出的模型的实用性和可行性。还讨论了乐观和悲观的版本。因此，设计了MCGDM问题（绿色供应商选择）的解决方案，方法是在两个范围内利用所提议的行为多粒度HFL DTRS，以及关键步骤和算法。最终，给出一个说明性的例子来阐述我们提出的模型的实用性和可行性。还讨论了乐观和悲观的版本。因此，设计了MCGDM问题（绿色供应商选择）的解决方案，方法是在两个范围内利用所提议的行为多粒度HFL DTRS，以及关键步骤和算法。最后，给出了一个说明性的例子来阐述我们提出的模型的实用性和可行性。