Evaluation - TOPSIS

1、 effect

TOPSIS Method is a commonly used comprehensive evaluation method within the group , Can make full use of the information of the original data , The results can accurately reflect the gap between evaluation schemes . The basic process is based on the normalized original data matrix , The cosine method is used to find the best scheme and the worst scheme in the finite scheme , Then calculate the distance between each evaluation object and the best scheme and the worst scheme respectively , The relative proximity between each evaluation object and the optimal scheme is obtained , Take this as the basis for evaluating the advantages and disadvantages . This method has no strict restrictions on data distribution and sample size , The data calculation is simple and easy .

2、 Input / output description

Input : At least two or more quantitative variables .
Output : Reflect the comprehensive score of assessment indicators in quantitative evaluation .

3、 Case example

In order to objectively evaluate the actual situation of graduate education in China and the teaching quality of graduate schools , The Academic Degrees Committee Office of the State Council, based on per capita monographs 、 Teacher student ratio 、 The scientific research funds and the overdue graduation rate shall be evaluated by the graduate school .

4、 Modeling steps

1. The original data is the same as the trend
Distinguish the indicator categories in the indicator system ( High or low priority ） , And according to different types of indicators, we need to carry out forward processing according to different formulas . structure n That's ok m Columns of the matrix X , Matrix It means the first one i The... Of the first object j Values of indicators . The conversion formula is as follows

Very small ：

The middle type ：

Interval type ：

2. Build a standardized matrix

3. Calculate the difference between each evaluation index and the best and worst vectors

among For the first time j Weight of attributes （ Analytic hierarchy process or entropy weight method ）.
4. Measure the closeness between the evaluation object and the optimal scheme

The bigger the value is. , It indicates that the better the evaluation object is