# Scheduling Optimization of Mobile Emergency Vehicles Considering Dual Uncertainties
## 4 Analysis of Examples
The compromise solution achieves the optimization of cost deviation as far as possible based on ensuring the minimization of time. The specific transportation paths of all types of \(K\) are shown in Figure 5. The path and demand uncertainty factors are controlled to verify the effectiveness of the mobile emergency vehicle scheduling model considering dual uncertainties. Suppose that the overall road condition deteriorates gradually; for example, the path complexity \(a_{ij}\) increases from 1.00-1.20 with step size 0.05, and the path integrity \(\beta_{ij}\) decreases from 1.00-0.80 with phase synchronization length. In other word, the path influence coefficient \(a_{ij}/\beta_{ij}\) is separately set to 1.00 (1.00/1.00), 1.11 (1.05/0.95), 1.22 (1.10/0.90), 1.35 (1.15/0.85), and  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multicolumn{2}{c}{**\(\mathbf{I1}\)**} & \multicolumn{2}{c}{**\(\mathbf{I2}\)**} & \multicolumn{2}{c}{**\(\mathbf{I3}\)**} & \multicolumn{2}{c}{**\(\mathbf{I4}\)**} \\ \cline{2-13}  & \(c_{0}^{h}\)/CNY & \(c_{0}^{h,s}\)/CNN & \(t_{0}^{1.50 (1.20/0.80). The scheduling results are shown in Figure 6. The overall decrease in road condition inevitably leads to a synchronous increase in the scheduling cost and time of mobile emergency vehicles. However, under different road conditions, the values of emergency costs and emergency time are relatively balanced: the overall optimal \(Z\) is within a reasonable range, which shows that the model considers the fairness of cost and time allocation. It is worth noting that in the process of the path impact coefficient rising, compared with the determinacy model, the increased ratio of mobile emergency scheduling time is similar to the value of the path impact coefficient. However, the increased proportion of mobile emergency scheduling costs is relatively slow. This means that emergency decision-makers should pay more attention to the time lag of the scheduling scheme of mobile emergency vehicles when considering the negative impact brought by route factors and give more weight to the emergency scheduling time when the road condition worsens. As for the demand uncertainty factor, the maximum disturbance value \(d_{j}^{\hat{E},u}\) is set to 50 and 100, respectively, and the example results are obtained when the control parameters \(\gamma_{j}^{k,u}\) are 0.1, 0.2, 0.3, 0.4, and 0.5, respectively. As shown in Table 5, the optimal solution values for mobile emergency scheduling cost and time increase with the increase in demand uncertainty. Under the premise of the same maximum disturbance value \(d_{j}^{\hat{E},u}\), the minimum distribution cost and the shortest distribution time have an increasing trend with the increase in the control coefficient \(\gamma_{j}^{k,u}\). Moreover, when the control coefficient \(\gamma_{j}^{k,u}\) is equal, the larger disturbance value will inevitably increase the model results. This means that the emergency decision-making can choose the control coefficient according to the actual demand floating range when dealing with the uncertainty demand further to determine the emergency center configuration and vehicle scheduling scheme. Figure 5: Determination of the optimal deployment path of the mobile emergency. Figure 6: Change in mobile emergency cost and time for different paths. Because of the randomness and strong destructiveness of sudden disasters, it is necessary to explore the comprehensive impact of dual uncertainties on the mobile emergency vehicle scheduling model based on considering the two types of uncertainties separately. In order to test the reliability of the model, four types of sudden disaster scenarios were designed. The first type is the scenario with weak path and demand uncertainties, \(\alpha_{ij}/\beta_{ij}=1.11\), \(d_{j}^{\xi,u}=100\). The second type of scenario is the scenario with strong path uncertainty and weak demand uncertainty, \(\alpha_{ij}/\beta_{ij}=1.35\), \(d_{j}^{\xi,u}=100\).