# Coordination in Emergency Response System Design:
## 7 Case Study: Hazmat Transportation in Chengdu City
### Improving Hazmat Emergency Response in Chengdu  
It is not possible to find an optimal solution to this large-scale problem instance with CPLEX within a reasonable time, and hence we apply the proposed genetic algorithm. The parameter setting is shown in Table 6.  
We start by focusing on the current situation, where each node is served by the closest emergency and ambulance teams. As mentioned in the previous section, the user preferences can help selecting the best emergency response plan based on one-to-one mapping among the covered nodes and ambulance-fire station pairs. Indeed, it is possible to improve 28 nodes from the adequate response time area to the preferred response time area through a simple reallocation of the resources. Figure 10 shows the response performance, under different user preferences, for the 28 nodes for which coverage was improved. This problem instance is shown in the first column of Table 7, where NA = 62 and NF = 20, i.e., when no new facilities are built. No nodes in the poor response time area, however, would benefit from such a reallocation of the hazmat incident response resources. \begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline
**[NA,NF]** & **[62,20]** & & **[62,25]** & **[62,30]** & **[62,35]** & **[67,35]** & **[72,35]** & **[77,35]** & **[82,40]** & **[87,45]** \\ \hline \hline \multirow{2}{*}{\# of New} & Ambance St. & 0 & 0 & 0 & 0 & 5 & 10 & 15 & 20 & 23 \\  & Fire St. & 0 & 5 & 10 & 15 & 15 & 15 & 15 & 20 & 24 \\ \hline \multirow{2}{*}{\# of Nodes} & Poor \(\rightarrow\) Pref. & 0 & 86 & 97 & 107 & 171 & 193 & 203 & 223 & 234 \\  & Adeq. \(\rightarrow\) Pref. & 28 & 30 & 35 & 37 & 36 & 35 & 36 & 39 & 40 \\ \hline \multirow{3}{*}{Minimum _RTAS_} & Ave. _RTAS_ (min) & 4.85 & 4.84 & 4.83 & 4.82 & 3.92 & 3.53 & 3.71 & 3.13 & 3.01 \\  & Ave. _RTFS_ (min) & 6.64 & 5.79 & 5.61 & 5.55 & 5.06 & 4.62 & 4.85 & 4.06 & 4.00 \\  & Ave. WT (min) & 3.12 & 2.38 & 2.20 & 2.16 & 2.03 & 1.90 & 1.96 & 1.72 & 1.64 \\ \hline \multirow{3}{*}{Minimum _RTFS_} & Ave. _RTAS_ (min) & 4.94 & 4.94 & 4.95 & 4.94 & 4.03 & 3.67 & 3.82 & 3.30 & 3.20 \\  & Ave. _RTFS_ (min) & 6.63 & 5.74 & 5.51 & 5.37 & 5.03 & 4.56 & 4.81 & 3.99 & 3.91 \\  & Ave. WT (min) & 3.09 & 2.37 & 2.15 & 2.08 & 1.98 & 1.85 & 1.90 & 1.68 & 1.56 \\ \hline \multirow{3}{*}{Minimum WT} & Ave. _RTAS_ (min) & 4.94 & 4.96 & 5.00 & 4.97 & 4.05 & 3.67 & 3.87 & 3.35 & 3.24 \\  & Ave. _RTFS_ (min) & 6.65 & 5.79 & 5.62 & 5.52 & 5.05 & 4.58 & 4.85 & 4.06 & 4.05 \\ \cline{1-1}  & Ave. WT (min) & 3.03 & 2.25 & 2.02 & 1.92 & 1.92 & 1.77 & 1.73 & 1.50 & 1.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Solutions with the genetic algorithm. Figure 9: The response and wait times in the Chengdu case. We now focus on another problem instance by setting NA = 82 and NF = 40, which amounts to allowing the construction of up to 20 new ambulance stations and 20 new fire stations. In the second to last column of Table 7, we see that the coverage on a total of 262 nodes can be improved to satisfy the response and wait targets. As a result, 87.4% of the city would enjoy a desirable level of coverage. Note that this amounts to an 89.1% improvement over the current situation. The optimal locations for the new ambulance and fire stations are depicted in Figure 11.  
Figure 11: The location of new fire and ambulance stations, when [NA, NF] = [82; 40]. Figure 10: When [NA, NF] = [62; 20], the 28 nodes for which coverage was improved: (**a**) before optimizing allocations; (**b**) minimum _RTAS_; (**c**) minimum _RTFS_; (**d**) minimum WT. Table 7 also depicts seven additional problem instances to provide the reader with a fuller picture. Arguably, the most interesting case is when [NA, NF] = [87, 45]. Although five new ambulance stations and five new fire stations can be established (compared to [NA, NF] = [82, 40]), the best coverage is obtained by building only three new ambulance stations and four new fire stations.