# Transportation and Reserve of Emergency Medical Supplies during Public Health Events
## 5 System Simulation Based on Six Cities in Jiangsu Province
### Simulation of the Urgency of Demand in Six Cities in Jiangsu Province
When the weights \(\varphi_{1}\) and \(\varphi_{2}\) in the total demand urgency function are set to 0.3 and 0.7, respectively, the total demand urgency \(Y_{k}\) of each city is calculated and normalized as Nanjing-0.1834, Wuxi-0.1603, Xuzhou-0.1537, Changzhou-0.1198, Suzhou-0.2291, and Nantong-0.1536. At the same time, the ranking of total demand urgency is shown in Table 13.  
It can be seen that in the early stage of the outbreak, according to news reports and data released by the Jiangsu Provincial Health and Health Commission, Suzhou was the first area with the largest outbreak and the most serious epidemic. At this time, the demand in Suzhou demand is the most urgent. Nanjing has Lukou International Airport and is the capital of Jiangsu Province, so the demand urgency is second. Wuxi is near Suzhou City and Shanghai City, and has a large flow of people and economy, so the demand urgency ranks third. Xuzhou City is located in the north of Jiangsu Province, and its economic ability is relatively poor, so its resistance ability is weak, and its demand urgency is fourth. Nantong and Changzhou, both near Suzhou, both have a high ability to fight against epidemic situation, so the demand urgency is low. The data are consistent with reality, and the feasibility and accuracy of the model are high. Simulation of the Transportation and Storage of Emergency Medical Materials in Six Cities in Jiangsu Province  
The city with the highest urgency of demand is taken as the dispatch point of emergency medical materials, that is, Suzhou is set as \(Y_{0}\). The map of six cities is shown in Figure 3.  
For convenience in the calculation, 50 vehicles are set as a group to meet the total demand of 6 cities. According to relevant references [48] and for this model, the parameters are set as shown in Table 14. \begin{table}
\begin{tabular}{c c c} \hline
**Sort** & **Demand Points** & **Demand Urgency** \\ \hline
1 & Suzhou (\(Y_{0}\)) & 0.2291 \\
2 & Nanjing (\(Y_{1}\)) & 0.1834 \\
3 & Wuxi (\(Y_{2}\)) & 0.1603 \\
4 & Xuzhou (\(Y_{3}\)) & 0.1537 \\
5 & Nantong (\(Y_{4}\)) & 0.1536 \\
6 & Changzhou (\(Y_{5}\)) & 0.1198 \\ \hline \end{tabular}
\end{table} 
Table 13: Table of ranking of the urgency of demand in six cities in Jiangsu Province. Taking Suzhou City, the distribution center, as the origin, establish the coordinates, select points with 25 km as a unit, and obtain the geographical position of each demand point, as shown in Table 15.  
PSO is used to solve the model and Python code is used to compile particle swarm optimization. \(P_{1}\) and \(P_{2}\) in the objective function of a single objective are set to 0.4 and 0.6, respectively. To solve the model, PSO is selected and the population number is set at 50, the inertia factor \(W\) is set at 0.2, the self-cognition factor \(C_{1}\) is set at 0.4, the social cognition factor \(C_{2}\) is set at 0.4 and the iteration times are 1000 times. The data calculated above are inserted into the simulation, and the conclusion is shown in Figure 4. \begin{table}
\begin{tabular}{c c c} \hline
**Demand Points** & **X** & **Y** \\ \hline Suzhou (\(Y_{0}\)) & 0 & 0 \\ Nanjing (\(Y_{1}\)) & 6.8 & 3.4 \\ Wuxi (\(Y_{2}\)) & 1 & 1 \\ Xuzhou (\(Y_{3}\)) & 12.2 & 12.9 \\ Nantong (\(Y_{4}\)) & \(-1\) & 3.2 \\ Changzhou (\(Y_{5}\)) & 2.2 & 2.2 \\ \hline \end{tabular}
\end{table} 
Table 15: Location settings of six cities in Jiangsu province. Figure 3: Map of six cities in Jiangsu province. It can be seen in Figure 4 that three batches of vehicles are used for transportation, the first batch is Suzhou-Nanjing-Suzhou, the second batch is Suzhou-Wuxi-Nantong-Suzhou and the third batch is Suzhou-Xuzhou-Changzhou-Suzhou. At this time, the global optimum is reached. The approximate optimal vehicle distance is 1461.45 km and the total cost is 6726.5 yuan. It proves the feasibility of the model designed in this paper. If more demand points are established, a more complex roadmap will be produced.