## 1 Introduction
Similarly, by employing advanced simulation techniques, including EM and GA, the authors of [17] proposed a methodology to ensure the smooth realization of robust and efficient adaptive manufacturing systems. Ref. [18] proposed a hybrid genetic algorithm that utilized a genetic algorithm to perform a global search and the particle swarm optimization algorithm to perform a local search. The semi-physical simulation technology of digital twins was utilized by [19] to achieve the optimization of the production line using the heuristic balance method. To improve precast production scheduling, ref. [20] developed a hybrid model based on the genetic algorithm and discrete event simulation. The study in [21] applied a genetic algorithm to support decision-making and operational production planning using the Tecnomatix Plant Simulation 2402 software. The paper in [22] proposed an integrated simulation as a holistic decision support tool. Optimization encompasses both manufacturing and service processes, such as intra-company transportation and storage. Many scholars have carried out research related to the optimization of the automated guided vehicle (AGV) transport system. The deployment of AGVs has significantly increased in recent years, driven by the intensification of automation and the adoption of Industry 4.0 principles. In one study, the authors of [23] presented a flexible framework to simulate transport systems based on AGVs. The framework used to perform the simulation also serves to implement the different policies and algorithms in the global control system. The study in [24] proposed an improved GA for omnidirectional AGV path planning that incorporates an ant colony algorithm (ACO). In the multi-AGV system, each collision avoidance decision has an impact on the efficiency of the system. To optimize the system's fitness regarding decisions, ref. [25] used the particle swarm optimization algorithm. Similarly, ref. [26] presented an easily adaptable method for collision and deadlock resolution in a multi-AGV transport system. A two-stage traffic scheduling scheme for a multi-AGV system with the use of a GA is presented in the paper by [27]. The paper in [28] introduced an initial model to demonstrate a possible approach for modeling the operation of freely moving AGVs by discrete event simulation. The AGV-hybrid task allocation problem in the assembly line was solved by [29], and real-time AGV scheduling on a flexible shop floor for material handling was addressed in [30]. When designing and optimizing an AGV transport system, it is necessary to consider various limitations regarding space, the number of AGVs, the loading area, and battery capacity. A model with the goal of minimizing the AGV path according to the AGV road network situation was solved by a genetic algorithm in [31]. Ref. [32] presented a scheduling solution that aimed to minimize the maximum completion time for the AGV scheduling problem in an intelligent warehouse. In addition, papers [33] and [34] studied the flexible job shop scheduling problem with a limited number of AGVs and proposed an improved genetic algorithm (IGA) to minimize makespan. The study in [35] proposed a dynamic scheduling method for task assignment and path optimization of AGVs to prevent collisions. The paper by [36] proposed a simulation-based multi-AGV scheduling procedure with limited buffer capacity and battery charging. In ref. [37], the authors designed an intelligent controller of a hybrid AGV while they used genetic algorithms to optimize the speed control strategy to improve efficiency and save energy. Modeling and optimizing AGV-based transport systems necessitates alignment with real operational conditions. An essential condition is the determination of the movement path. The existing literature extensively addresses AGVs operating along predefined paths. Some studies focus on freely navigating AGVs within specified points, where the exact path is not exactly specified.