## 2 Materials and Methods
### Finding the Optimal Route  
During the operation of the algorithm, the \"local best fit\" values are found among the individuals in the current population by looking at the fitness score in each iteration, and they are compared and updated with the \"global best fit\" value (that is, the best-fit individual found considering all previous populations). When the algorithm stops, the route to which the global best value belongs is named \"the fittest\" and recorded as the result found by the algorithm. Figure 4: (**a**) Deciding the vehicle to be sent for delivery. (**b**) Drone assignment for delivery. (**c**) The truck continues on the route and meets the drone. Within the scope of the paper, a solution is provided for the distribution of more than one different type of cargo with more than one same or different type of drone working hybrid with a truck. In this respect, considering that there are multiple drones, it is decided that the relevant drone will deliver by taking into account the current battery charge rate of the drones, the distances required by subsequent delivery tasks, the characteristics of the location where the cargo will be distributed and the route status parameters of the truck. The basic algorithmic structure of the GA used in the article is given in Algorithm 1. ```
Genetic algorithm with dynamic operators () { Initialize population randomly; Evaluate fitness of each individual in the population; While stopping condition met { Perform selection; Perform crossover and dynamic mutation; Evaluate fitness of each individual; Change selection, crossover and mutation operators. } }
```  
**Algorithm 1**The basic algorithmic structure of the genetic algorithm. The operators and explanations of the genetic algorithm used for the proposed approach can be given as follows: Average/Min-Max crossover has been used as the crossover operator and consists of the change of behavior between the two parents based on the average of the absolute processing time of the chromosome segments [36]. The main role of the mutation operator in the genetic algorithm is to create new genetic information and produce new individuals that will enable the main solution to be approached. A high probability of mutation may help produce better production, but in larger than average individuals, this may lead to the loss of good individuals. In adaptive mutation, individuals above the average in a population mutate with a very low probability, and individuals below the average mutate with a high probability. The parameters of the genetic algorithm used in the article are given in Table 1 below. To increase the understandability of the approach proposed in this paper, a numerical example is given below, as shown in Figure 5. In the numerical example given below (Table 2), an example with six nodes (a, b, c, d, e, and f) has been selected. For these nodes, one central node (a), two intermediate nodes (b, c), and three customer nodes (d, e, f) have been created. The number of drones, the number of customers, the time the drones can stay at a node during the distribution mission, the battery capacities of the drones, and the weight capacities of each drone are shown in the table. In this numerical example, it is taken into account that drones can move at three different altitudes. In the scenario consisting of one truck and two drones, the results obtained if the first drone makes one distribution and the second drone makes two distributions are given in Table 2b. In addition, the time graph obtained from the results obtained with ten different scenarios of these numerical examples is given in Figure 6. As can be seen here, in the truck-drone hybrid system, as the capacity of the drones increases, the time duration decreases significantly.