## 1 Introduction
### Motivation  
Vehicle Routing Problem (VRP) and Traveling Salesman Problem (TSP) are the most well-known studies, which are relatively old methods and are used to reduce costs by finding the optimum route in the distribution method made only by trucks. TSP is a problem in which a seller who has products that need to be distributed to different cities with known distances between each other uses a vehicle to deliver these products to all delivery points in the shortest way possible without repetition. TSP is basically an optimization problem and is defined as an NP-hard problem. VRP is a generalized version of TSP, and it is a problem that aims to minimize the cost by finding the most suitable route, as in TSP. Furthermore, VRP is defined as an NP-hard problem like TSP. VRP differs from TSP in that it can include multiple routes rather than a single route, and it also considers vehicle capacity limitations [10; 11; 12]. Another delivery method that is becoming widespread in transportation today is the use of drones as well as the traditional method. Studies on the use of drones, which are used in many fields, such as logistics and transportation, date back to the early 2000s in the literature. A study conducted in 2004 focused on aerial cargo robots, and the first flight of the prototype of these aerial cargo robots was carried out in 2005 [13]. Drones are preferred due to their advantages, such as being lightweight, being able to work in large areas in the field without the need for any human control, and having fast movement capabilities. In addition to these advantages, there are also disadvantages, such as the difficulty of coordinating multiple drones that will work together and the difficulty of calculating the most appropriate route. Due to the stated advantages of drones, their use is increasing day by day in the transportation and logistics sector, as well as in other sectors [14; 15]. For example, in the study [16], an optimization study was carried out that allowed multiple transportation with only a single drone without using a truck. The TSP algorithm was used to determine the order in which drone deliveries should occur, with a maximum of three deliveries being made at the same time. It was planned that customers would receive the package by scanning the QR code on the drone camera during the delivery phase. Here, not proposing a truck-drone delivery-based approach and delivering using only one drone instead of multiple vehicles was seen as a shortcoming. Therefore, the algorithm proposed in this study was developed to overcome its shortcomings. In terms of delivering cargo to more than one customer at the same time, instead of just trucks or only drones, the application of hybrid trucks and multiple drones is increasing today and is seen as an inevitable solution in the future. Especially with the growth of electronic commerce, the intensification of city traffic, and the formation of high-rise buildings, the hybrid truck multi-drone application has been developed as one of the new cargo distribution solutions. In terms of truck and drone hybrid studies in the literature, the carrying potential of drones has not been sufficiently taken into account in mathematical modeling; the fact that drones can have different flight altitudes has not been adequately reflected; and in truck and multi-drone studies, only energy consumption, package weight, and drone weight are taken into account. In this article, a dynamic genetic algorithm-based approach has been discussed, especially in terms of ensuring this synchronization, and various results have been obtained for the mathematically modeled system. Drones can fly at various altitudes and offer lower-cost solutions for cargo delivery. In this study, a GA-based hybrid truck multi-drone system was created to reduce delivery time by presenting a different perspective for truck-drone delivery.