## 4 Case Application
### Basic Data and Results Analysis  
Tables 1 and 2 show the initial decision-making matrix and reference point sequence values of enterprise Z for five science and technology talents. The data in Table 1 are the evaluation values under the input indicators and the data in Table 2 are the evaluation values under the output indicators. The indicators that this paper uses are usually applied in the comparison of scientific and technological enterprises. The parameter values that this paper selects are drawn from the actual data of Z enterprise, which can be obtained directly from the recruitment market, materials and website and the human resource department, along with other sources. They have good applicability and can potentially be expanded and verified by further experiments. The evaluation value of this paper is represented by an interval grey number. There are two ways to express interval grey numbers, one being the conventional data range expression (see the data on the left of each column in the table) and the other being the combination expression of a kernel and the degree of greyness (see the data on the right of each column in the table). Because the indicators in this paper are all of benefit type, the original data in Tables 1 and 2 are used to calculate the uniform effect measure by Formula (9) and grey number algorithm. According to the maximum deviation method in the M-1 model, Formula (12) and the uniform effect measure value, the indicator weights are calculated, as shown in Tables 3 and 4. The data in Table 3 are input indicator weights and the data in Table 4 are output indicator weights. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Input** & **Salary** & **Insurance** & **Welfare** & **Housing** & **Funds** \\ \hline S\({}_{1}\) & [15, 20] & 17.5\({}_{0.25}\) & [9, 10] & 9.5\({}_{0.08}\) & [4, 6] & 5.0\({}_{0.33}\) & [22, 30] & 26.0\({}_{0.13}\) & [10, 15] & 12.5\({}_{0.17}\) \\ S\({}_{2}\) & [20, 22] & 21.0\({}_{0.10}\) & [12, 14] & 13.0\({}_{0.15}\) & [4, 7] & 5.5\({}_{0.50}\) & [30, 35] & 32.5\({}_{0.08}\) & [25, 32] & 28.5\({}_{0.24}\) \\ S\({}_{3}\) & [16, 30] & 23.0\({}_{0.70}\) & [11, 13] & 12.0\({}_{0.15}\) & [5, 8] & 6.5\({}_{0.50}\) & [40, 50] & 45.0\({}_{1.07}\) & [18, 28] & 23.0\({}_{0.34}\) \\ S\({}_{4}\) & [25, 30] & 27.5\({}_{0.25}\) & [13, 18] & 15.5\({}_{0.38}\) & [7, 9] & 8.0\({}_{0.33}\) & [60, 65] & 62.5\({}_{0.08}\) & [20, 35] & 27.5\({}_{0.52}\) \\ S\({}_{5}\) & [22, 24] & 23.0\({}_{0.10}\) & [10, 13] & 11.5\({}_{0.23}\) & [4, 6] & 5.0\({}_{0.33}\) & [45, 50] & 47.5\({}_{0.08}\) & [10, 20] & 15.0\({}_{0.34}\) \\ C\({}^{(1)}\) & [12, 30] & 21.0\({}_{0.90}\) & [8, 15] & 11.5\({}_{0.54}\) & [3, 6] & 4.5\({}_{0.50}\) & [20, 70] & 45.0\({}_{0.83}\) & [10, 30] & 20.0\({}_{0.69}\) \\ C\({}^{(2)}\) & [12, 20] & 16.0\({}_{0.40}\) & [7, 13] & 10.0\({}_{0.46}\) & [4, 7] & 5.5\({}_{0.50}\) & [10, 30] & 20.0\({}_{0.33}\) & [6, 15] & 10.5\({}_{0.31}\) \\ C\({}^{(3)}\) & [15, 30] & 22.5\({}_{0.75}\) & [6, 15] & 10.5\({}_{0.69}\) & [5, 8] & 6.5\({}_{0.50}\) & [25, 60] & 42.5\({}_{0.58}\) & [10, 20] & 15.0\({}_{0.34}\) \\ C\({}^{(4)}\) & [10, 20] & 15.0\({}_{0.50}\) & [5, 15] & 10.0\({}_{0.77}\) & [3, 8] & 5.5\({}_{0.83}\) & [25, 50] & 37.5\({}_{0.42}\) & [15, 30] & 22.5\({}_{0.52}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation value and reference point value under input indicators (unit: CNY 10,000).