考虑可信度和优先级的犹豫模糊信息集成算子

Hesitant fuzzy information aggregation operators considering confidence level and priority level

研究了具有专家可信度与属性优先级的犹豫模糊信息集成问题,对每一个犹豫模糊数给出相应的可信度,用来表示专家对属性的熟悉程度。考虑到专家可信度的重要影响,提出了考虑专家可信度的犹豫模糊熵值算法,并在此基础上结合属性优先级提出了考虑专家可信度和属性优先级的混合赋权方法,该赋权方法既能保证属性优先级恒定,又可以有效区分专家意见的统一程度以及专家对属性的熟悉程度。之后,在此基础上给出了考虑专家可信度与属性优先级的犹豫模糊信息集成算子,并给出了基于该类算子的多属性决策方法。

The hesitant fuzzy information aggregation operators are investigated, in which the attributes and experts confidence are in different priority levels. Each evaluation value provided by experts for the projects has a corresponding confidence level denoting the degree that the experts are familiar with the attribute. Considering the double impact of confidence level and the dispersion degree of attribute elements, a hesitant fuzzy entropy method is presented, and then a prioritized hybrid weighted method is proposed based on the hesitant fuzzy en- tropy method. The weighted method could guarantee a constant priority level of the attribute and distinguish the unity degree of expert opinions effectively and the degree that the experts are familiar with the attribute. Then, the hesitant fuzzy information aggregation operators based on the hybrid weighted methods are put forward. Furthermore, a hesitant fuzzy multiple attribute decision-making method based on the proposed operators is developed.