研究专家权重未知的对偶犹豫模糊多属性群决策问题. 以记分函数和精确度函数为基础, 引入广义距离测度, 定义一种对偶犹豫模糊数的全序关系. 而后, 为克服现存算子的不足, 定义新的Hamacher运算法则, 并提出改进的对偶犹豫模糊Hamacher加权 (A-DHFHW) 算子, 分析算子与参数的内在关系. 为获取专家的客观权重, 基于距离测度定义群共识测度, 建立以最大化群共识测度为目标的权重优化模型. 进一步地, 基于全序关系、权重优化模型以及A-DHFHW 算子提出一种新的群决策方法. 通过解决大数据分析平台的评价问题验证所提方法的有效性和实用性, 并分析参数对决策结果的影响.

The multiple attribute group decision-making problems with unknown experts' weights are studied. Based on the score and accuracy functions, the generalized distance measure is introduced to define a total order relation, which can distinguish arbitrary different dual hesitant fuzzy numbers. Then, in order to overcome the shortcomings of the existing operators, some new Hamacher operational laws are defined, and the adjusted dual hesitant fuzzy Hamacher weighted (A-DHFHW) operators are proposed, and the intrinsic relationship between the operators and the parameter is analyzed. To obtain the object weights of the experts, the optimization model is constructed by maximizing the distance measure-based group consensus. Furthermore, a new group decision making method is proposed based on total order relation, weight optimization model and the A-DHFHW operators. Finally, the effectiveness and practicability of the proposed group decision method are verified by solving the evaluation problem of the large data analysis platform, and the influence of the parameters on the decision results is analyzed.