# Robust Optimization of Transport Organization for China Railway Express
## 4 Example Analysis
### Parametric Sensitivity Analysis  
#### 4.2.1 Sensitivity Analysis of Different Robustness Level Parameters  
The \(\Gamma\) parameter is used to control the robustness level of the model in the established China-Europe freight transport organization optimization model so as to obtain optimal results under different risk preferences. The nodes with uncertain freight demand are composed of 16 cities: Zhengzhou, Chongqing, Chengdu, Xi'an, Nanjing, Xuzhou, Suzhou, Lianyungang, Nantong, Wuhan, Changsha, Yiwu, Hefei, Shenyang, Dongguan, and Lanzhou. The freight demand of each freight node \(i\) is disturbed \(\varepsilon_{i}=0.2\), and the freight demand of the node is shown in Table 5. The robustness level parameters are changed to solve the model, and the corresponding \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Node Spacing (km)** & **Zhengzhou** & **Chongqing** & **Chengdu** & **Xi’an** & **Urumqi** & **Moscow** \\ \hline Zhengzhou & 0 & 1086 & 1157 & 495 & 2998 & 6724 \\ Chongqing & 1086 & 0 & 333 & 745 & 2749 & 6475 \\ Chengdu & 1157 & 333 & 0 & 676 & 2660 & 6386 \\ Xi’an & 495 & 745 & 676 & 0 & 2505 & 6231 \\ Urumqi & 2998 & 2749 & 2660 & 2505 & 0 & 3726 \\ Nanjing & 721 & 1343 & 1649 & 1167 & 3678 & 7404 \\ Xuzhou & 381 & 1444 & 1503 & 829 & 3338 & 7064 \\ Suzhou & 940 & 1565 & 1873 & 1382 & 3892 & 7618 \\ Lianyungang & 548 & 1618 & 1692 & 1030 & 3537 & 7263 \\ Nantong & 870 & 1597 & 1906 & 1336 & 3827 & 7553 \\ Wuhan & 504 & 836 & 1145 & 963 & 3665 & 7391 \\ Changsha & 859 & 832 & 1356 & 1253 & 3517 & 7243 \\ Yiwu & 1131 & 1560 & 2081 & 1591 & 4106 & 7832 \\ Hefei & 568 & 1185 & 1493 & 1003 & 3460 & 7186 \\ Shenyang & 1383 & 2930 & 2577 & 1914 & 4078 & 7804 \\ Dongguan & 1531 & 1289 & 1635 & 1987 & 4525 & 8251 \\ Lanzhou & 1032 & 873 & 813 & 577 & 1867 & 5593 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Transport distance between network nodes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Nodes** & **Weekly Freight Volume (TEU)** & **ID** & **Nodes** & **Weekly Freight Volume (TEU)** \\ \hline
1 & Zhengzhou & 414 & 9 & Nantong & 50 \\
2 & Chongqing & 479 & 10 & Wuhan & 210 \\
3 & Chengdu & 506 & 11 & Changsha & 172 \\
4 & Xi’an & 232 & 12 & Yiwu & 157 \\
5 & Nanjing & 252 & 13 & Hefei & 126 \\
6 & Xuzhou & 106 & 14 & Shenyang & 108 \\
7 & Suzhou & 249 & 15 & Dongguan & 59 \\
8 & Lianyungang & 547 & 16 & Lanzhou & 117 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Nodal freight demand. transport organization scheme is shown in Table 6. The specific values of uncertain cost and total transport cost under different robustness levels are shown in Table 7, depicted in Figure 2.  
From the above research results, it can be seen that when the robustness level is \(\Gamma=0\), it means that the freight demand of all freight nodes in the model is determined, and the model is equivalent to the China-Europe freight train transportation organization optimization model in a certain environment. At this time, the model does not generate perturbations of uncertain costs, the total cost of transportation. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Bank Opening Mode**} & \multirow{2}{*}{**Opening Line**} & \multicolumn{2}{c}{**Opening Frequency (Column\(\cdot\)Week\({}^{-1}\))**} \\ \cline{3-4}  & & **Domestic Road Transport Train** & **Transnational Road Transport Train** \\ \hline Zhengzhou–Moscow & 0 & 6 \\ Chongqing–Moscow & 0 & 7 \\ Chengdu–Moscow & 0 & 7 \\ Xi’an–Moscow & 0 & 4 \\ Nanjing–Moscow & 0 & 4 \\ Xuzhou–Moscow & 0 & 2 \\ Suzhou–Moscow & 0 & 3 \\ Through transport & Lianyungang–Moscow & 0 & 8 \\ organization model & Nantong–Moscow & 0 & 1 \\ Wuhan–Moscow & 0 & 3 \\ Changsha–Moscow & 0 & 3 \\ Yiwu–Moscow & 0 & 3 \\ Hefei–Moscow & 0 & 2 \\ Shenyang–Moscow & 0 & 2 \\ Dongguan–Moscow & 0 & 1 \\ Lanzhou–Moscow & 0 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Train operation plan.