Abstract: For the multi-attribute decision-making (MADM) problems that the attribute weights are completely unknown under the interval-valued normal fuzzy environment, two interval-valued normal fuzzy information aggregation algorithms are designed, a novel interval-valued normal fuzzy MADM model is investigated. First, the interval-valued normal fuzzy operational laws are defined. Then, from the arithmetical and geometric point of view, the interval-valued normal fuzzy weighted averaging operator (IVNFWA) and interval-valued normal fuzzy weighted geometric operator (IVNFWG) are proposed. The relationship between these two operators is analyzed. Finally, combining these two proposed operators with distances, a new model is developed to deal with the interval-valued normal fuzzy MADM problems in which the attribute weights are completely unknown, and then apply the developed model to research on the selection of database system. The results obtained from performance analysis show that the proposed approach is correct, feasible and efficient.

Key words: Interval-valued normal fuzzy number, information aggregation algorithm, multi-attribute decision making, database system

摘要： 在区间正态模糊信息环境下，对于指标权重信息完全未知的多属性群决策问题，设计了两种信息集成算法，建立一种区间正态模糊多属性群决策模型。首先给出区间正态模糊数的基本运算规则；然后，分别从算术和几何角度设计了区间正态模糊加权平均(IVNFWA)算子和区间正态模糊加权几何(IVNFWG)算子，同时对新建立的2个算子进行大小比较分析；最后在区间正态模糊信息环境下将提出的信息集成算法与距离公式相结合，构建一种属性权重完全不可知的多属性决策模型；结合数据库系统选择实例，对提出的决策方法进行验证。测试结果表明，提出的多属性决策方法是正确的、可行的与高效的。

关键词: 区间正态模糊数, 信息集成算法, 多属性决策, 数据库系统