The aim of the paper is to solve the group decision making problems which contain inconsistent probabilistic linguistic preference relations (PLPRs) and unknown expert weights. When the PLPRs are inconsistent, there are contradictories in the preference relations expressed by the experts. The evaluation value with contradictory information will bring out an incorrect consequence in decision making. Hence, this paper develops a novel consistency measure method to gauge the consistency level of PLPRs. Moreover, a nonlinear optimization model is newly constructed to optimize the inconsistent PLPRs. The proposed methods overcome the limitations in the existing methods and ameliorate the interpretation and complexities of inconsistency PLPRs revise strategies. Additionally, a weighting method using fuzzy cooperative games with PLPRs is put forward to derive the weight vector of experts. It helps to balance the deviations between the individual PLPRs and the group PLRP. At last, a numerical example illustration for physician selection is put forward to demonstrate the effectiveness of the proposed model and its practical applicability. The comparative analysis gives deep insights into the rationality of the proposed model.

本文的目的是解决包含不一致的概率语言偏好关系（PLPR）和未知专家权重的群体决策问题。当PLPR不一致时，专家所表达的偏好关系就存在矛盾。带有矛盾信息的评估值将给决策带来不正确的后果。因此，本文开发了一种新颖的一致性度量方法来度量PLPR的一致性级别。此外，新构建了非线性优化模型来优化不一致的PLPR。所提出的方法克服了现有方法的局限性，并改善了PLPR不一致修改策略的解释和复杂性。另外，提出了一种基于PLPR的模糊合作博弈加权方法，得出专家的权重向量。它有助于平衡各个PLPR和组PLRP之间的偏差。最后，给出了一个用于医师选择的数值示例说明，以证明所提出模型的有效性及其实际适用性。比较分析对所提出模型的合理性提供了深刻的见解。