## 5 Conclusions  
Location-allocation models are one of the most important forms of GIS analysis and are used in many applications. To simulate the real world more effectively, it is necessary to add the objectives and criteria required to solve the problem into the problem space to obtain optimal solutions. One of these criteria is the criterion of capacity. Adding this condition to the problem of locating and allocating emergency facilities, such as fire stations, will lead to the optimal selection of stations in relation to the capacity of each station, thereby allowing for optimal service. First, the VAOMP model was developed to include the capacity criterion, and then the results of the VAOMP model were investigated with the VAOCMP model to examine the status of the existing fire stations in the study area, with the aim of minimizing arrival time using the Tabu Search and Simulated Annealing algorithms. The result in scenario 1 shows that 10 existing stations in the area were not enough to serve 336,600 existing demands and that 52,840 demands remained without service. Thus, 35 candidate stations were added to the analytical processes by locating potential fire stations. The processes showed that 13 stations are able to serve all demands, while the Tabu Search algorithm produces better results. This research used the VAOMP model developed by \"[PERSON] and [PERSON], 2014\", and the research results showed that the model could produce good and close results with exact methods with a huge amount of data. Too, the results of this study are consistent with the results of [PERSON] et al. [1], who sought to optimize airports with the VAOMP model using the Tabu search algorithm. This new model can find optimal positions well, and like [PERSON] et al. [1] Tabu algorithm show good results. According to [PERSON] et al. [14], the Tabu algorithm works better than the Simulated Annealing algorithm in solving location-allocation problems and has a better speed in achieving optimal solutions. With the larger data set, the VAOMP model was used, and results showed with 28 fire stations, 99.30% of demands can be allocated. Again, the results of using the VAOCMP model in Scenario 2 showed that 10 stations in the study area were not able to service 59,080 demands in the area. Therefore, the candidate stations generated in Scenario 1 were used again, and the results showed that the 13 stations (10 existing stations and \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of Fire Stations**} & **Solving Time Based on** & **Solving Time Based on** \\  & **Tabu Search** & **Simulated Annealing** \\ \hline
26 & 715.228 & 852.210 \\
27 & 782.140 & 901.825 \\
28 & 840.371 & 949.414 \\ \hline \hline \end{tabular}
\end{table} 
Table 26: Comparison of the average problem-solving time in seconds. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of Fire Stations**} & **Accuracy of Allocation Based on** & **Accuracy of Allocation Based on** \\  & **Tabu Search** & **Simulated Annealing** \\ \hline
26 & 84 & 79 \\
27 & 79 & 77 \\
28 & 78 & 73 \\ \hline \hline \end{tabular}
\end{table} 
Table 24: Comparison of the accuracy of allocation results. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of Fire Stations**} & **Average Percentage of All** & **Average Percentage of All** \\  & **Demand Allocated Based on** & **Demand Allocated Based on** \\  & **Tabu Search** & **Simulated Annealing** \\ \hline
26 & 82.676 & 81.009 \\
27 & 93.103 & 90.338 \\
28 & 98.990 & 97.730 \\ \hline \hline \end{tabular}
\end{table} 
Table 25: The average percentage of all demand allocated using both algorithms. 3 candidate stations) were able to service to 99.5% of the region's demand. Again, with the larger data set (1,250,796 persons), the VAOCMP model was used, and results showed with 28 fire stations, 98.36% of demands can be allocated. In this scenario, the Tabu Search algorithm produces better results than the Simulated Annealing algorithm in solving this kind of Median problem.