基于离差最大化与距离最小化的专家权重确定方法

Based on the Difference between Maximizing and Minimizing the Distance from Determining the Weights Experts

该文提出了一种基于离差最大化与属性权重向量距离最小化的多属性群决策中专家权重确定的新方法。首先,根据专家给出的客观评价决策矩阵,通过熵权法计算出属性权重值。然后,根据计算得到的属性权重向量构成属性向量矩阵,建立专家函数,求出对应专家权重。其次,再采用离差最大化的方法计算出相应专家的权重。最后,将由距离最小化与离差最大化的方法得到的专家权重值进行加权集成综合,进而给出多方案间的排序。最后,给出算例对其进行说明该方法的可行性与实用性。

This paper proposed a idea based on maximizing deviation and minimization from the attribute weight vector method, this method is actually a new method for establishing the multi-attribute group decision making expert weight. First, according to the experts give an objective evaluation of the property matrix, using entropy method to calculate attribute weights. Then, according to the attribute weight vector calculated constitute property vector matrix, to establish expert weighting function, find the corresponding expert weights. Secondly, the system uses maximizing deviation method to calculate the weight of the specialist. Finally, by minimizing the distance with attribute weights , The expert weight of maximizing deviation combined with expert weight of minimizing attribute weights to determine the final expert weight and then give sorting among programs. This method give the maximizing deviation method of objectivity, and the expert weights and attribute weights to establish direct contact, interaction between the two, so that the weight of experts are more objective. Finally, numerical examples illustrate its feasibility and practicality of the method.