## 5 Numerical Results
### Numerical Case  
To validate the performance of the model and algorithm, numerical simulation experiments were used to test different problem instances. This was assuming that there are multiple emergency demands within a certain area of the freeway (\(A_{1}\), \(A_{2}\), \(A_{3}\), \(A_{4}\), \(A_{5}\)), with different response levels. The types (\(G_{1}\), \(G_{2}\), \(G_{3}\), \(G_{4}\)) and numbers of EVs required for each disaster site, as well as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Resource Sites** & **EVs Type** & **Fire Truck** & **Ambulance (\(G_{2}\))** & **Police Car (\(G_{3}\))** & **Engineering Rescue** \\  & **(G1)** & **Ambulance (\(G_{1}\))** & **Ambulance (\(G_{2}\))** & **Police Car (\(G_{3}\))** & **Vehicle (\(G_{4}\))** \\ \hline \(S_{1}\) & 10 & 15 & 10 & 20 \\ \(S_{2}\) & 15 & 15 & 10 & 25 \\ \(S_{3}\) & 20 & 10 & 5 & 20 \\ \(S_{4}\) & 15 & 20 & 5 & 15 \\ \(S_{5}\) & 20 & 10 & 10 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Transportation time (min). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Instances** & **Resource Sites** & **Disaster Sites** & **EVs Type** & **The Dimensions of the Solution Space** \\ \hline Small-sized & \(S_{1}\)\(\sim\)\(S_{3}\) & \(A_{1}\)\(\sim\)\(A_{3}\) & \(G_{1}\)\(\sim\)\(G_{4}\) & \(3\times 3\times 4=36\) \\ Large-sized & \(S_{1}\)\(\sim\)\(S_{5}\) & \(A_{1}\)\(\sim\)\(A_{5}\) & \(G_{1}\)\(\sim\)\(G_{4}\) & \(5\times 5\times 4=100\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Categories of test instances. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resource Sites** & **EVs Type** & **Fire Truck** & **Ambulance (\(G_{2}\))** & **Police Car (\(G_{3}\))** & **Engineering Rescue** \\  & **(G1)** & **Ambulance (\(G_{2}\))** & **Police Car (\(G_{3}\))** & **Vehicle (\(G_{4}\))** \\ \hline \(S_{1}\) & 2 & 2 & 4 & 2 \\ \(S_{2}\) & 2 & 4 & 2 & 4 \\ \(S_{3}\) & 3 & 4 & 5 & 2 \\ \(S_{4}\) & 4 & 2 & 4 & 4 \\ \(S_{5}\) & 3 & 5 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Resource reserves at site. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Parameter Setting** & **The Dimensions of the Solution Space** \\ \hline ADEPSO & population size = 50, \(T_{max}=300\), \(c_{1}=c_{2}=2\), \(w=0.9\)\(\sim\)0.2 \\ PSO & population size = 50, \(T_{max}=300\), \(c_{1}=c_{2}=2\), \(w=0.9\)\(\sim\)0.2 \\ GA & population size = 50, \(T_{max}=300\), mutation and crossover probability = 0.1, 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Parameters of three algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resource Sites** & **EVs Type** & **Fire Truck** & **Ambulance (\(G_{2}\))** & **Police Car (\(G_{3}\))** & **Engineering Rescue** \\  & **(G1)** & **Ambulance (\(G_{2}\))** & **Police Car (\(G_{3}\))** & **Vehicle (\(G_{4}\))** \\ \hline \(S_{1}\) & 10 & 15 & 10 & 20 \\ \(S_{2}\) & 15 & 15 & 10 & 25 \\ \(S_{3}\) & 20 & 10 & 5 & 20 \\ \(S_{4}\) & 15 & 20 & 5 & 15 \\ \(S_{5}\) & 20 & 10 & 10 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Dispatch cost. EVs scheduling scheme analysis  
Due to space limitations, only the scheduling scheme for the large-sized instance is presented. Table 9 compares the comprehensive cost and average rescue waiting time (ARWT) of the schemes found by the three algorithms. The ADEPSO algorithm proposed in this paper solves the CER problem with a significantly better solution quality than the basic PSO and GA algorithms, and finds a better scheme in terms of the comprehensive cost and ARWT. In particular, the GA solves the problem with a 12.7% lower comprehensive cost and a 4% lower ARWT than the PSO. Furthermore, the ADEPSO achieves a comprehensive cost 26.3% lower and an AWRT 10% lower than the PSO. Table 10 displays the optimal schemes derived by the three algorithms. The ADEPSO algorithm's scheduling scheme is more efficient because each resource site dispatches EVs only to the three to four closest disaster sites.