# Robust Optimization of Transport Organization for China Railway Express
## 4 Example Analysis
### Parametric Sensitivity Analysis
#### 4.2.2 Sensitivity Analysis of Different Freight Demand Disturbance Ranges  
In the established optimized model for the controllable robustness level of cargo transportation demands in the China-Europe freight train transportation organization, it is assumed that the cargo transportation demands at each node follow a box-type uncertainty distribution. The disturbance level of freight transportation demands at node \(i\) is denoted as \(\varepsilon_{i}\). Specifically, \(U_{i}=\left[\overline{Q_{i}}-\overline{Q_{i}}\cdot\varepsilon_{i},\overline{Q _{i}}+\overline{Q_{i}}\cdot\varepsilon_{i}\right]\). The nodes affected by uncertain freight demands comprise sixteen cities: Zhengzhou, Chongqing, Chengdu, Xi'an, Nanjing, Xuzhou, Suzhou, Lianyungang, Nantong, Wuhan, Changsha, Yiwu, Hefei, Shenyang, Dongguan, and Lanzhou.  
The robustness parameter \(\Gamma\) is set to 5. The freight transportation demand at the nodes is detailed in Table 5. By varying the disturbance level of freight demands \(\varepsilon_{i}\), the total transportation costs under different disturbance levels are computed. Additionally, optimal transportation organization schemes under varying disturbance levels are presented in the following tables.  
When the disturbance level of freight transportation demand \(\varepsilon_{i}=0\), the model represents a deterministic environment for transportation organization optimization. In this scenario, the total transportation costs, transportation organization schemes, and the results obtained align with those of a deterministic environment.  
When the disturbance level of freight transportation demand lies between 0.1 and 1 (\(0.1\leq\varepsilon_{i}\leq 1\)), the corresponding train operation schedule is presented in Table 8.  
Figure 2: Costs at different levels of robustness.  
With an incremental increase in disturbance levels from \(\epsilon_{i}=0.1\) to \(\epsilon_{i}=0.6\), the total transportation cost for cargo transportation also escalates, rising from CNY 86,478,780 in a deterministic environment to CNY 167,253,300. This increase occurs due to a consistent robustness level (\(\Gamma=5\)). As the disturbance levels (\(\epsilon_{i}\)) at various cargo transportation nodes rise, the China-Europe freight train network can only counteract this expanding disturbance by augmenting the uncertainty transportation costs within the transportation organization, aiming to maintain the optimal solution across the entire transportation network.  
When the disturbance level in cargo transportation further increases to a point where \(\epsilon_{i}\geq 0.7\), the uncertainty transportation costs will escalate. To minimize the total transportation cost, adjustments will be made to the optimization plan of the freight train transportation organization. This adjustment leads to a change in the model's optimal solution. At this stage, some freight trains will be consolidated and rerouted through Urumqi, resulting in a significant reduction in overall transportation costs.  
Based on the analysis of the uncertain cargo demand disturbance, it is evident that there is a strong correlation between the total transportation cost of goods and the disturbance level (\(\epsilon_{i}\)) in cargo demand. To counter the increase in disturbance level (\(\epsilon_{i}\)), the model will maintain the optimal solution by augmenting the uncertainty cost. As the disturbance level (\(\epsilon_{i}\)) further increases, the model will re-search new optimization strategies for freight train organization to attain the optimum solution. At this point, the transportation network will reach a renewed stable state.