## 2 Literature Review  
In the literature, several studies have approached AGVs and AGV systems from various perspectives. [PERSON] (2006) reviewed the existing methods in the literature and reported that models developed to design and control AGV systems could overcome certain prob lems, but new approaches were required to overcome long computation times and other problems such as congestion, deadlock, and delays in extensive AGV systems [16]. An AGV moves on a path which is fixed or variable depending on the AGV type. The path assignments affect the number of AGVs. [PERSON] et al. proposed the employment of an artificial neural network to determine the shortest path for a single AGV. It was aimed to determine the minimum total empty travel time. Good findings were reported for computation time. Although the algorithm achieved acceptable solutions, none of them were optimal or near optimal in certain sequential models [17]. In another work, the AGV path was analyzed with the Ant Colony Optimization (ACO) algorithm, and it was demonstrated that the route determined with ACO improved the efficiency within an acceptable time [18]. [PERSON] et al. proposed a split delivery vehicle routing problem to minimize vehicle travel on a path. They compared memetic heuristic and mathematical models [19]. The management of AGV traffic is another significant problem. Traffic management entails the prevention of collisions and deadlocks that could block the AGV system. Management could be conducted with a central control system or various routing methods. [PERSON] et al. (2007) proposed a decomposition method with a cut generation to efficiently solve collisions or deadlocks across AGVs [20]. [PERSON] et al. presented a dynamic routing method for multiple AGVs with time windows. In the case of a collision on the selected route at a specific time, the algorithm looks for another route. The efficiency of the method was demonstrated by simulation [21]. [PERSON] et al. suggested the use of real-time AGV and route monitoring, which allowed the system to run AGVs online and avoid problems such as deadlocks [22]. [PERSON] suggested the employment of new-generation AGVs during deadlocks that would allow \(\theta^{\circ}\) around the center of a circle, and after the transition, the AGV would return to its original position [23]. [PERSON] proposed a system where the AGV could change its load in the case of a deadlock. These studies improved AGV system flexibility, albeit by increasing the AGV cost, while maintenance problems negatively affected the system performance [24]. Routing strategies and rules affect the number of AGVs in the system, as well as other factors such as the Pick & Deposit (P&D) location. Vehicle requirements entail the number of AGVs required to fulfill system transport demands. This issue is also known as fleet sizing. According to [PERSON], the fleet is sized based on transport load demands, time demands, AGV capacity, AGV speed, routing, traffic management, AGV assignment, and P&D locations [16]. [PERSON] and [PERSON] emphasized the response time as a factor in fleet size calculations. The response time is the idle travel time until the next pickup. They tried to minimize the reaction time [25]. The minimization of this non-value-added time would reduce the total transport time. The minimization of the maximum response time with positioned waiting points in an AGV system was analyzed by [PERSON] et al. to minimize the average response time. A single loop pattern was analyzed and compared with the mathematical model and genetic algorithm approach, and the authors observed that the computation time increased with the model size and the GA could be optimized with the mathematical model [26]. [PERSON] et al. proposed a distributed routing method with motion delay disorder to minimize the transport time with multiple AGVs, which reduced the collision probability and collision-related penalties. The findings demonstrated that the proposed method could be implemented in real transportation environments [27].