Probabilistic linguistic term set solves the problem of probabilistic distribution of linguistic terms. Due to the objective and subjective factors such as the decision makers’experience and preference, the credibility of the linguistic terms is different. However, current studies on PLTSs ignore this difference. In this paper, we first propose a novel concept called Z probabilistic linguistic term set (ZPLTS). As an extension of existing tools, it takes advantage of the fact that Z-number can represent both information and corresponding credibility. At the same time, we discuss the normalization, operational rules, ranking method and distance measure for ZPLTSs. Then, we propose a new weight calculation method, an aggregation-based method and an extended TOPSIS method, and apply them to multi-attribute group decision making in Z probabilistic linguistic environment. Finally, a numerical example and some comparisons with other methods illustrate the necessity and effectiveness of the proposed method.

概率语言术语集解决了语言术语的概率分布问题。由于决策者的经验和偏好等客观和主观因素，语言术语的可信度有所不同。但是，当前对PLTS的研究忽略了这种差异。在本文中，我们首先提出一个新颖的概念，称为Z概率语言术语集（ZPLTS）。作为现有工具的扩展，它利用了Z值既可以表示信息又可以表示相应信誉的事实。同时，我们讨论了ZPLTS的规范化，操作规则，排序方法和距离度量。然后，我们提出了一种新的权重计算方法，一种基于聚集的方法和一种扩展的TOPSIS方法，并将其应用于Z概率语言环境中的多属性小组决策。最后，通过算例和与其他方法的比较说明了该方法的必要性和有效性。