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针对指标权重未知的混合型多属性决策问题,提出一种基于二元语义的决策方法.首先,定义了语言评价变量与三角模糊数的转化规则和二元语义之间的距离,给出了不同类型指标数据与二元语义的转化;然后,利用与正理想解灰色关联度偏差最小原理,确定了属性的指标权重,并利用二元语义加权算术平均值对方案进行排序;最后,通过应用案例说明了所提方法的决策步骤,并与TOPSIS方法进行了比较,表明了所提方法的有效性和优越性.
Aiming at the problem of mixed multi-attribute decision making with unknown index weight, a decision-making method based on binary semantics is proposed.Firstly, the transformation rules of linguistic evaluation variables and triangular fuzzy numbers and the distance between binary semantics are defined, Type index data and binary semantics. Secondly, using the minimum principle of deviation from the positive ideal solution, the index weights of the attributes are determined and the binary semantic weighted arithmetic mean is used to rank the programs. Finally, through the application cases The decision-making steps of the proposed method are illustrated and compared with the TOPSIS method, which shows the effectiveness and superiority of the proposed method.