## 3 Model Construction and Solving Algorithm
### Solving Algorithm
#### 3.2.2 Application of NSGA-II-TOPSIS-GCA Algorithm for MOODPR Problem
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**A**} & \multicolumn{2}{c}{**B**} & \multicolumn{2}{c}{**C**} & \multicolumn{2}{c}{**D**} \\ \cline{2-9}
**Scheme No.** & \begin{tabular}{c} **Hybrid** \\ **Closeness** \\ **Degree** \\ \end{tabular} & \begin{tabular}{c} **Scheme** \\ **Sort** \\ \end{tabular} & \begin{tabular}{c} **Hybrid** \\ **Closeness** \\ **Degree** \\ \end{tabular} & \begin{tabular}{c} **Scheme** \\ **Sort** \\ \end{tabular} & \
\begin{tabular}{c} **Hybrid** \\ **Closeness** \\ **Degree** \\ \end{tabular} &
\begin{tabular}{c} **Scheme** \\ **Sort** \\ \end{tabular} \\ \hline
1 & 0.6919 & 71 & 0.5632 & 99 & 0.5243 & 35 & 0.6796 & 31 \\
2 & 0.6802 & 14 & 0.5563 & 94 & 0.5231 & 36 & 0.6794 & 97 \\
3 & 0.6715 & 44 & 0.5538 & 95 & 0.5230 & 37 & 0.6751 & 1 \\
4 & 0.6706 & 18 & 0.5481 & 80 & 0.5229 & 1 & 0.6699 & 95 \\
5 & 0.6681 & 30 & 0.5476 & 81 & 0.5216 & 38 & 0.6599 & 14 \\
6 & 0.6664 & 84 & 0.5467 & 100 & 0.5198 & 39 & 0.6589 & 54 \\
7 & 0.6660 & 74 & 0.5462 & 96 & 0.5175 & 40 & 0.6589 & 61 \\
8 & 0.6644 & 37 & 0.5461 & 82 & 0.5155 & 92 & 0.6587 & 43 \\
9 & 0.6591 & 98 & 0.5435 & 83 & 0.5144 & 41 & 0.6559 & 87 \\
10 & 0.6524 & 79 & 0.5419 & 84 & 0.5140 & 42 & 0.6552 & 75 \\
11 & 0.6520 & 59 & 0.5408 & 7 & 0.5117 & 43 & 0.6272 & 71 \\
12 & 0.6519 & 3 & 0.5404 & 97 & 0.5094 & 93 & 0.6248 & 93 \\
13 & 0.6483 & 28 & 0.5390 & 8 & 0.5089 & 44 & 0.6230 & 70 \\... &... &... &... &... &... &... &... &... \\
99 & 0.3270 & 65 & 0.4657 & 64 & 0.3746 & 33 & 0.3139 & 52 \\
100 & 0.3094 & 1 & 0.4647 & 1 & 0.3717 & 34 & 0.2878 & 55 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hybrid closeness degree of schemes and their sorts for four scheme sets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scheme Sets** & **Decision Scheme No.** & \(\alpha\) & \(\gamma\) & \(\
u\) & **WSI** \\ \hline A & 71 & 0.806 & 0.857 & 0.748 & 5.073 \\ B & 99 & 0.611 & 0.663 & 0.880 & 20.963 \\ C & 35 & 0.556 & 0.429 & 0.938 & 30.610 \\ D & 31 & 0.498 & 0.305 & 0.987 & 40.024 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The attribute indexes of final decision schemes for four scheme sets. In addition to the total amount of each objective, its time allocation process is also an important basis for objectively reflecting the water supply capacity and ecological protection. Figure 8 shows the ecological and comprehensive water supply processes in different typical years. The comprehensive water demand included urban-rural water and irrigation water. The urban-rural water process was relatively stable, while the irrigation water was relatively unstable, which was mainly affected by the irrigation system. Therefore, the peaks of the comprehensive water supply were consistent with the peaks of irrigation water, which were mainly concentrated in January, March, May, and July. Figure 9 shows the satisfaction degree of ecological water supply per ten-day period from May to April of the next year in different typical years. From the wet year to the extraordinarily dry year, the satisfaction degree of the ecological water supply gradually decreased, and the number of unsatisfactory periods increased significantly, which mainly occurred in the non-flood season.