基于灰熵模型的区间型指标和权重的不确定多属性决策方法及其应用

区间型属性值及权重多属性决策问题的难点在于不确定权重信息的精确化和区间数的排序问题.灰熵模型中运用与理想解均衡接近的贴近度对方案排序的思想,不仅可以使多属性决策避开繁重的模糊数据精确化步骤,还可以有效解决方案排序时的点关联倾向问题.考虑到传统灰熵模型只适用于精确实数和指标权重缺失的缺陷,将灰关联熵引入传统灰熵模型,构建区间型权重属性值的灰熵模型,解决不确定数据精确化的难题.针对区间数排序难的难题,基于TOPSIS方法应用衍生变量接近度、均衡度再次逼近理想解的思想计算改进灰熵模型的均衡接近度对方案进行排序.最后,通过SG激光装置项目选择某种非标元器件供应商的算例验证了所提出模型的有效性.

The uncertain multi-attribute decision making methods and application based on grey entropy model with interval-type attribute values and weights

The difficulties of multi-attribute decision making with interval-type attribute values and weights lie in the precision work of uncertain weight information and the ordering of interval numbers. The idea of using the approach degree to approach the ideal solution in the grey entropy model can not only avoid the troublesome steps of precising fuzzy data in multi-attribute decision making, but also can effectively solve the problem of local correlation. Considering that the traditional grey entropy model is only applicable to the situation where the attribute values are all exact real numbers and index weights are lacked, the grey relation entropy is introduced into the traditional grey entropy model to construct the grey entropy model with interval weights and attribute values, which successfully solves the problem of precising uncertain data. In order to solve the difficult problem of ordering interval numbers, this model makes the two derived variables approach the ideal solution again based on the TOPSIS method to calculate the degree of balance and approach to sort the solution. Finally, the effectiveness of the model is verified by the example of SG laser device project in selecting a non-standard component supplier.