基于灰类敏感度系数的评价指标客观权重极大熵配置模型

评价指标权重的确定是多属性决策问题中至关重要的环节。然而,既有研究利用评价指标值之间的差异性进行指标的客观赋权,往往忽略了评价指标对被评价对象全体及所在系统的重要性。本文基于事物自然本质属性差异,运用灰色关联聚类将评价对象划分到预设的类别;借鉴有无对比分析法的思想,定义了反映指标对全体被评价对象类别影响程度的灰类敏感度系数;根据极大熵准则,建立了基于灰类敏感度系数的客观权重极大熵配置模型,以确定多属性决策指标的权重。并通过与文献[21,26]中实际案例的对比分析,说明了本模型的有效性与更贴近现实性,为解决多属性决策指标客观赋权问题提出了一个新思路。

The Maximum Entropy Configuration Model of Objective Index WeightBased on Grey Class Sensitivity Coefficient

To determine the weight values of assessment indexes is the crucial link in multiple attribute decision making problems. However, today’s researches have used the evaluation index difference, which tends to ignore the evaluation index of the importance for the evaluation object and the system. Based on the natural essence attribute differences, this article uses the grey correlation clustering to classify the evaluation object into the default category, and by the analysis method of being with or without contrast, defines the grey class sensitivity coefficient which influences the impact of the indexes for the evaluation of all the object classes; and based on the maximum entropy criterion,sets up the objective weight maximum entropy configuration model based on the grey class sensitivity coefficient to determine the weight of multiple attribute decision making. Through the contrast analysis of actual cases in ref. [21,26], it shows the effectiveness and closeness to the reality of this model, which has put forward a new thought for solving multiple attribute decision making problem.