## 3 Results
### Multi-Drone Results  
The samples from the TSPLIB library used in Table 3 are then simulated using GA with different drone number parameters, and Table 4 is created. The population size is set to 200, the number of iterations to 1000, and the drone speed to be twice the truck speed. The drone range is not restricted because the areas covered by the cities used in the simulation and the distances between each other vary greatly. The purpose of the difference in the number of drones from scenario to scenario is to observe the effect of the number of drones on the result obtained. The table is created according to the shortest time, average time, and longest time values obtained as a result of running each scenario ten times. As the number of drones used increases, it is seen in Figure 10 that the delivery time tends to decrease. Some values appear to overlap in the figure because they are smaller and closer to each other than the clearly visible values. In addition, when the results of another simulation performed on the same routes are examined in Table 3 and compared with the results in Table 4, it is determined that the results obtained in the scenarios using drones on the same dataset are smaller than the results of the scenario solved like a standard TSP (as in Table 3). This simulation demonstrates the importance of using drones for deliveries. The locations of the cities for the test routes in the six different data sets used are given on the coordinate plane in the figures below. In addition, in the case of using one drone, the most suitable routes obtained for the drone and truck in terms of the shortest time values in Table 4 are also shown in the figures. Oliver30 is a dataset of 30 samples in a 100 \(\times\) 100 area taken from TSPLIB [37]. In Figure 11, the locations of the cities to be delivered in the Oliver30 sample data set are given in the coordinate plane. According to TSP, the length of the optimum route that can be found in its solution is 423. In Figure 12, the route with the shortest time value is given in Table 4 and is made using one drone with the algorithm proposed in this study. The black lines on the route in Figure 12 show the route of the truck, and the red dashed lines show the drone route. The value found for this route is 268.57. The starting points of the truck are indicated in all relevant Figures with a black star icon. \begin{table}
\begin{tabular}{l l l l l} \hline
**Sample** & **Parameter** & \(n=1\) & \(n=2\) & \(n=3\) \\ \hline \multirow{3}{*}{Oliver30} & Shortest Time & 268.57 & 225.53 & 199.22 \\  & Average Time & 286.17 & 230.7 & 203.11 \\  & Longest Time & 298.89 & 237.55 & 204.34 \\ \hline \multirow{3}{*}{Eil51} & Shortest Time & 275.86 & 228.95 & 197.79 \\  & Average Time & 293.42 & 233.6 & 205.43 \\  & Longest Time & 301.79 & 236.37 & 213.24 \\ \hline \multirow{3}{*}{Berlin52} & Shortest Time & 5464.90 & 4547.6 & 3851.57 \\  & Average Time & 5769.94 & 4676.11 & 3992.83 \\  & Longest Time & 5970.97 & 4817.53 & 4094.61 \\ \hline \multirow{3}{*}{st70} & Shortest Time & 485.96 & 403.75 & 345.95 \\  & Average Time & 497.2 & 411.49 & 360.44 \\  & Longest Time & 517.53 & 427.88 & 378.7 \\ \hline \multirow{3}{*}{Eil76} & Shortest Time & 395.67 & 309.23 & 260.54 \\  & Average Time & 412.61 & 316.04 & 265.98 \\ \cline{1-1}  & Longest Time & 430.79 & 329.88 & 272.2 \\ \hline \multirow{3}{*}{KroA100} & Shortest Time & 19,788.83 & 16,207.64 & 13,718.63 \\ \cline{1-1}  & Average Time & 20,593.88 & 16,786.1 & 14,017.2 \\ \cline{1-1}  & Longest Time & 21,083.2 & 17,577.64 & 14,755.49 \\ \hline \end{tabular}
\end{table} 
Table 4: Calculation results for scenarios with different numbers of drones. Eil51 is a dataset of 51 samples in a 70 \(\times\) 70 area taken from TSPLIB [37]. In Figure 13, the locations of the cities to be included in the Eil51 sample set are given in the coordinate plane. According to TSP, the length of the optimum route to be found is 429.