## 4 Discussion and Conclusions  
The use of drones in transportation, besides being environmentally friendly, also shortens delivery times considerably, as seen in this study. In addition, their use continues to become more widespread day by day, thanks to their ability to deliver to regions with geographical restrictions. With the increased use of drones, there are drone-containing versions of the problems of calculating the shortest route, which plays an important role in delivery. There are different scenarios in the literature where drones are included in the delivery problems. The scenario discussed in this study is the hybrid single-truck multi-drone scenario. This scenario is based on the coordinated operation of one truck and \(k\) drones. The most basic and important feature of this scenario is that the drone that leaves the truck and goes to the delivery makes an appointment with the truck at one of the next delivery points. In this context, different simulations have been created, results have been obtained, and comparisons have been made. First of all, route calculations have been made for a TSP by taking six different test routes in the first simulation. Two different optimization algorithms, PSO and GWO, and the algorithm proposed in this study have been evaluated together. The obtained data have been given in the tables in a comparative way according to the optimum result, and the success of the proposed GA-based algorithm has been clearly seen in the tables. As a result of the comparisons made in terms of error rate, it has been observed that a lower error rate occurred with GA. Moreover, since the running time is the determining factor for the calculation of the most appropriate route when an evaluation is made in this respect, the proposed GA-based algorithm has shown that it  
Figure 31: A solution where a drone is used and the traffic situation is also taken into account. Figure 32: Schematic representation for a solution for the multi-truck multi-drone problem using a proposed solution of single-truck multi-drone problem in this paper. is more advantageous than other algorithms in terms of running time. In this case, the proposed GA-based solution manages to calculate the most suitable route with the shortest working time and the least error rate, which reveals why it was preferred in this study. Afterward, the same simulation was performed again for the same six different test routes, taking into account the variable number of drones. The obtained results have been evaluated comparatively in figures and tables. The results clearly show that as the number of drones increases, the delivery time gets shorter. This situation clearly emphasizes the importance of multi-drone use, which is the main purpose of this study. In addition, the cities in these six data sets have been shown on the coordinate plane, and the most suitable route for the use of a single truck and a single drone has been given in the visuals. Also, in this section, the proposed algorithm in the scenario created has been tested with a previous study in a similar field, and the results have been given. With the test and comparison, it has been seen that the proposed method is superior to the previous similar study in the literature. Finally, the proposed algorithm has been applied to a real-world problem. Traffic data for randomly determined delivery points in the mapped region has been obtained from the internet, and deliveries have been expected to take place using one truck and a variable number of drones. The operation of the proposed algorithm has been analyzed by examining the situations where the drone is used and the situations where the traffic density is taken into account or not. When the results are examined, drones are directed to areas with heavy traffic with the proposed method, and it is ensured that the most suitable route is found in the shortest time and with the least cost in solving a hybrid truck multi-drone problem in the real world.