Currently, TOPSIS is one of the most commonly used comprehensive assessment methods(Lai et al., 1994). This method can make full use of the information from original data, and the results can accurately reflect the differences among the objects. Moreover, there is no strict requirement on data distribution and sample size. However, the TOPSIS models do not reflect the relative importance for different indexes, i.e., it assigns equal weights to all indexes. IE is used to calculate the dispersion degrees for index. Specially, if one index has a smaller information entropy value, it will be assigned to a larger weight. Therefore, this research combines the IE to assign weights to indexes in TOPSIS model, which is named as IE-TOPSIS method. The processes of IE-TOPSIS are as follows: 检查是否有语法错误

我没有发现任何语法错误。这段话介绍了TOPSIS方法是一种常用的综合评价方法，可以充分利用原始数据信息，结果可以准确反映对象间的差异。同时，该方法对数据分布和样本大小没有严格要求。但是，TOPSIS模型没有反映不同指标的相对重要性，即它将所有指标分配相等的权重。为了解决这个问题，该研究将信息熵（IE）应用于TOPSIS模型中，以对指标进行加权，提出了IE-TOPSIS方法。该方法的过程如下所述。