## 5 Discussion  
The current research has been conducted to provide a network of facilities for locating the hub by considering existing facilities and new facilities under possible scenarios. Also, the appropriate and reasonable coverage radius and the optimal capacity level are obtained by solving the proposed model of this research. According to the studies, a mathematical model was presented for locating the hub by considering the probability of possible scenarios. More precisely, in this research, the proposed model provides solutions  
\begin{table}
\begin{tabular}{c c} \hline
**Index** & **A Significant Difference Between the Averages of the Indicators** \\ \hline Computational time & Yes \\ Average of the first objective function & No \\ Average of the second objective function & No \\ NPF & No \\ MSI & No \\ SM & No \\ \hline \end{tabular}
\end{table}
Table 18: Overview of the significant difference between the average comparison indicators.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm** & \begin{tabular}{c} **First** \\ **Objective** \\ **Functions** \\ \end{tabular} & \begin{tabular}{c} **Sum of the** \\ **Second and** \\ **Third Objective** \\ **Functions** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Feasible** \\ **Solutions** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Spread** \\ **Indicator** \\ \end{tabular} & \
\begin{tabular}{c} **Diversity** \\ **Distance** \\ **Indicator** \\ \end{tabular} &
\begin{tabular}{c} **Computational** \\ **Time** \\ \end{tabular} \\ \hline NSGA II & 4,526,072 & 45,240 & 20.27 & 2,478,417 & 0.623 & 844 \\ MOPSO & 4,497,208 & 45,418 & 17.27 & 2,260,667 & 0.557 & 1041 \\ Weight & 0.4 & 0.4 & 0.05 & 0.05 & 0.05 & 0.05 \\ \hline \end{tabular}
\end{table}
Table 19: Average indices obtained from meta-heuristic algorithms.  
to improve demand coverage by examining the available facilities in the network. Improvement solutions include the following: establishing new facilities, removing existing facilities, and making changes in existing facilities. In the proposed model, the probability of occurrence of possible scenarios for locating the hub is considered. By solving the model, the characteristics of each facility including the level of the facility in the hierarchical structure, the coverage radius of the facility, and its capacity level are determined. Finally, in order to solve the model in real dimensions and in an acceptable time, the exact solution algorithm and the meta-heuristic algorithm NSGAII and MOPSO have been implemented in the model.  
According to the analysis of two meta-heuristic algorithms, NSGAII and MOPSO, to evaluate the mathematical model, it was shown that the NSGAII algorithm has a better performance.  
The practical applications of the proposed model are significant, particularly for telecommunications companies seeking to optimize their network planning. By considering real-world constraints such as environmental disruptions and facility capacity limits, this model helps to make better decisions in site selection for BTS. Furthermore, its adaptability to fluctuating demand and environmental considerations makes it highly applicable in dynamic urban environments and disaster management. The model can also be extended to other industries where facility location plays a key role, such as in logistics and emergency services, thus offering broad utility across various sectors.  
The results of this study demonstrate the practical applicability of the proposed model for telecommunication network planning. By optimizing BTS site selection within a green supply chain framework, the model provides actionable insights for reducing environmental impact while ensuring cost-effective service delivery. These findings align with the industry's growing focus on sustainability and resource efficiency.