## 6 Experimental Tests
### Case Study: Application of HCLP in Humanitarian Logistic Services
Parameters \(q_{l}\), \(c_{l}\), \(f_{i}\), and \(h_{i}\) are generated randomly using uniform distribution between (30 and 50), (200,000 and 300,000), (700,000 and 900,000), and (4,000,000 and 6,000,000), respectively. As mentioned in the assumptions of the model and as a real-life fact, when a disaster hits a region, the modules dispatch to that area to provide service for demands requests until the end of the tactical periods, and in the next strategic period, as the new disaster occurs, they leave toward the newly affected area for the new mission. Therefore, in this case study, the threat scenarios are designed in this way that firstly one disaster hits the South-Central (R1) in the first strategic period; the second disaster occurs in the North-Central part (R2) in the second strategic period, and in the third strategic period, it is the Central part (R3) that is affected by a disaster and needs the modules assignment. Figure 3: Different disaster operational phases and supply chain decision categories. Figure 4 shows the demand points in green color and the located facilities in blue in the last strategic period. The arrows show the flow of the modules from previously affected areas to the newly affected one to provide service. The blue circles have been used to demonstrate the affected areas. According to the solution of the problem for these threats, the first area (R1) affected by the disaster could cover 86.8% of the demands, the coverage for the second area (R2) was 99.6%, and in the last affected area (R3), the coverage of demand points was 43.3% by the limited number of available modules. Table 2 includes the results of this case study in the second row. The coverage percent is calculated as:  
\[\frac{\sum_{i,j,l,t,\tau}g_{ij}d_{jlt\tau}x_{ijlt\tau}}{\sum_{j,l,t,\tau}d_{ jlt\tau}}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problem Data** & **Operating Facilities in R1, R2, and R3** & **Coverage \%** & **MZ** & **MY** & **Obj** & **Time** \\ \hline  & R1: Amagasaki, Izumi, Kakogawa, Kawanishi, Kishiwada, Nara, Sakai, Suita, Takatsuki, Wakayama, Yao. \(cp_{l}\): U (200k, 300k) & R1: 86.8\% & R2: 99.6\% & 51.6 & 33.9 & 256M & 165 \\ \(f_{l}\): U (70k, 90k) & Morioka, Sendai. \(h_{l}\): U (4M, 6M) & R3: 43.3\% & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular}
\end{table} 
Table 2: Computational results of case study and sensitivity analysis. Figure 4: The demand points and facilities located at the last strategic period. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Problem Data**} & \multicolumn{2}{c}{**Operating Facilities in R1, R2,**} & \multirow{2}{*}{**Coverage \%**} & \multirow{2}{*}{**MZ**} & \multirow{2}{*}{**MY**} & \multirow{2}{*}{**Obj**} & \multirow{2}{*}{**Time**} \\  & & & & & & \\  & & & & & & \\ \hline R1: Amagasaki, Kakogawa, & & & & & & \\ Kishiwada, Nara, Sakai, & & & & & & \\ Suita, Takatsuki. & & & & & & \\ R2: Aomori, Fukushima, & & & & & \\ R2: Aomori, Fukushima, & & & & & \\ R2: 37.5\% & 50.6 & 39.9 & 22M & 71 \\ R3: 4.3\% & & & & & \\ R3: 4.3\% & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Cont. The second column of Table 2 shows the operating facilities in each strategic period for R1, R2, and R3. The columns \"MZ\" and \"MY\" contain the average number of located facilities and modules, respectively. Columns \"Obj\" and \"Time\" include the objective value and elapsed time to solve the problem. Other rows of Table 2 contain the results for some sensitivity analysis of capacity and cost parameters. In the second solved problem, the capacities of the modules decreased, which resulted in a significant coverage percentage for all regions and objective values. The number of located facilities does not change so much, but the problem tries to provide more coverage by assigning more modules. In the third problem, the effect of increasing the facility location cost was the main purpose.