Abstract: Aiming at it is difficult to provide accurately and fully decision maker’s evaluation information by using hesitant fuzzy information in the real decision-making situation, the Probability Uncertainty Hesitant Fuzzy Preference Relation（PUHFPR） is first introduced, which can effectively deal with the Probability Uncertain Hesitant Fuzzy Element（PUHFE） decision problem elements in probability information partly known and completely unknown. Then, the expected additive consistency and acceptable expected additive consistency of PUHFPR are defined, and an optimization model is constructed to determine the probability of occurrence of PUHFPR elements by minimizing the deviation. In addition, based on consistency adjusted algorithm, the probability uncertainty hesitant fuzzy decision-making model is developed to derive priority weights of alternatives. Finally, an example of the investment through the selection of listed companies shows the effectiveness of the decision-making model.

Key words: probability uncertainty hesitant fuzzy preference relations, expected additive consistency, consistency adjusted algorithm, decision-making model

摘要： 针对犹豫模糊信息在现实决策中难以准确和充分的提供决策者评价信息的问题，引入了概率不确定犹豫模糊偏好关系（PUHFPR）的概念，其能够有效处理概率不确定犹豫模糊元（PUHFE）中元素发生概率信息部分已知和完全未知的决策问题；给出了PUHFPR的期望加行一致性、满意加性期望一致性定义，并以偏差最小化为目标函数构建最优化模型确定PUHFPR元素的发生概率；建立基于一致性调整算法的概率不确定犹豫模糊偏决策模型，得到方案的排序权重向量，从而选择最佳的方案；通过遴选上市公司进行投资的实例说明决策模型的有效性。

关键词: 概率不确定犹豫模糊偏好关系, 期望加性一致性, 一致性调整算法, 决策模型