## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Injured** & \(\textbf{U}_{I}^{inc}\) & \multicolumn{5}{c}{**The Severity of the Traumatic State**} \\ \cline{2-7}
**Number \(I\)** & \(\textbf{c}_{I,1}\) & \(\textbf{c}_{I,2}\) & \(\textbf{c}_{I,3}\) & \(\textbf{c}_{I,A}\) & \(\textbf{c}_{I,S}\) & \(\textbf{c}_{I,6}\) \\ \hline
1 & \(1,6\) & \(2\) & \(15\) & \(11\) & \(1\) & \(0\) & \(30\) \\
2 & \(15\) & \(4\) & \(3\) & \(1\) & \(1\) & \(0\) & \(34\) \\
3 & \(8,14\) & \(3\) & \(9\) & \(8\) & \(1\) & \(0\) & \(22\) \\
4 & \(8\) & \(2\) & \(15\) & \(10\) & \(0\) & \(0\) & \(24\) \\
5 & \(11\) & \(3\) & \(14\) & \(10\) & \(1\) & \(0\) & \(46\) \\
6 & \(1,5\) & \(1\) & \(15\) & \(12\) & \(0\) & \(1\) & \(27\) \\
7 & \(7\) & \(2\) & \(15\) & \(10\) & \(0\) & \(0\) & \(50\) \\
8 & \(11,12\) & \(3\) & \(11\) & \(8\) & \(1\) & \(0\) & \(57\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The severity of the traumatic state (Equations (11) and (16)). Taking into account the above assumptions, the situation shown in Figure 1 is obtained. The victims in the mass casualty incident are assigned to groups based on their identified health status using the quantities given in Section 3.5. This assignment is summarized in Table 8.  
The presented problem of optimizing the allocation of forces and resources to injured persons was programmed in the LINDO API using the B and C algorithms to search for local minimum values of the objective function. The number of binary decision variables is equal to the power of the feasible solutions: \(L\times B\times S\times R=8\times 4\times 3\times 2=192\), while the number of non-zero decision variables, which is the optimal solution, is equal to the number of injured persons (\(L=8\)). The example was solved using the weighted sum method in the LINDO API. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of the Transport Mode** & **Transport Mode** & **Average Speed \(V_{r}\) of the \(r\)-th Transport Mode in** \\  & **Mode \(r\)** & **Kinometers per Hour** \\ \hline
1 & air & 60 \\
2 & ground & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The average speed of the MRT mode (as defined in Equation (33)). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**HED’s Number s**} & **Distance from the HED \(h_{s,1}\) to the Scene of the MCI \(z_{1}\) in** & **Number of** & **A Set of Types of** \\  & **Kilometers** & **Injured People That Can** & **Post-Accident Injuries** \\  & **for the Ground Type of the** & **for the Air Type of the** & **Be Handled by the \(s\)-th** & **Handled by the \(s\)-th** **HED \(h_{s,3}\)** \\  & **MRT (\(r=1\))** & **MRT (\(r=2\))** & **HED \(h_{s,2}\)** & \\ \hline
1 & 5 & 3 & 10 & 1, 7, 8, 9, 10, 11, 12 \\
2 & 12 & 10 & 10 & 3, 4, 5, 6, 7, 14 \\
3 & 7 & 3 & 10 & 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Current possibilities of HEDs (as defined in Equation (2)). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of the**} & **Distance from the MRTB \(a_{b,1}\) to the Scene of** & **Number of** & **Number of the \(r\)-th** \\  & **the MCI \(z_{1}\) in Kilometers** & **Mean of the MRT from** \\
**MRT \(b\)** & **for the Ground Type** & **for the Air Type of** & **the \(b\)-th MRTB \(a_{b,2,r}\)** \\  & **of the MRT (\(r=1\))** & **the MRT (\(r=2\))** & **the \(b\)-th MRTB \(a_{b,2,r}\)** \\ \hline
1 & 10 & 7 & \textless{}10,10\textgreater{} \\
2 & 12 & 10 & \textless{}10,10\textgreater{} \\
3 & 7 & 3 & \textless{}10,10\textgreater{} \\
4 & 14 & 8 & \textless{}10,10\textgreater{} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MRTB characteristics (as defined in Equation (7)). Then, the multi-criteria optimization problem in the form of Equation (41) reduces to the following one criteria Mixed Integer Linear Programming problem:  
\[\min_{l,b,s,r}\Bigl{\{}Z\left[\stackrel{{\rightarrow}}{{x}}(l,b,s,r )\right]\Bigr{\}}, \tag{48}\]  
with the constraints defined in Equations (42)-(45).