## 1 Introduction  
In modern aerospace manufacturing, the efficiency of production and the effectiveness of resource allocation in assembly workshops are critical determinants of product delivery timelines and quality standards [1, 2]. With increasing structural complexity, the trend toward larger components, and the growing demand for customization, traditional workshop management and production scheduling models fall short of meeting manufacturing demands for high efficiency, precision, and flexibility [3, 4, 5]. Consequently, a new approach that integrates intelligent logistics, lean production principles, and system-level simulation is urgently required to systematically optimize the aerospace assembly process. In recent years, a growing body of research has focused on integrating Automated Guided Vehicles (AGVs) with modern production scheduling systems to overcome the inefficiencies and rigidity associated with traditional manual transportation methods [6]. Owing to their high efficiency, flexibility, and level of automation, AGV systems have been widely adopted across various production settings, including automotive manufacturing,electronic assembly, and the aerospace industry [7, 8]. Conventional AGV scheduling approaches primarily rely on priority-based rules [9] and path planning algorithms [10], such as shortest-path algorithms based on graph theory [11], genetic algorithms [12, 13], and ant colony algorithms [14]. Prior studies have demonstrated that AGV scheduling not only reduces material transportation time but also significantly improves the flexibility and automation level of production systems [15, 16]. For example, [PERSON] et al. proposed a Mixed-Integer Programming (MIP) model to minimize job completion time in AGV scheduling and significantly enhanced scheduling efficiency using a customized branch-and-bound algorithm [17]. [PERSON] et al. optimized the AGV scheduling model by introducing a bidirectional conflict-free routing strategy, which demonstrated high efficiency in real-world applications at automated container terminals [18]. [PERSON] et al. presented a scheduling model that considers battery constraints and a heterogeneous AGV fleet, and solved it using Mixed-Integer Linear Programming and a large neighborhood search algorithm to optimize transport and charging tasks [19]. [PERSON] et al. integrated the dynamic job shop scheduling problem (DJSP) with AGVs by formulating it as a Markov Decision Process (MDP) and optimizing it with a Deep Q-Network (DQN) algorithm, showing superior performance compared to rule-based dispatching methods [20]. However, most existing studies concentrate solely on AGV scheduling, often overlooking the coordination between AGVs and other production resources--such as workstations, personnel, and machinery--in complex production environments. This limitation becomes particularly pronounced in assembly workshops constructing complex products, where large component sizes and complex processes create bottlenecks that cannot be resolved by AGV scheduling alone [21, 22]. Although researchers have called for integrated optimization approaches, the coordination between AGVs and other resources remains insufficiently explored. Discrete Event Simulation (DES) has been widely employed in the modeling and optimization of manufacturing systems [23]. By simulating different production scenarios, researchers can assess the impact of various scheduling strategies and resource allocation schemes in a virtual environment, ultimately identifying the most efficient production processes [24]. Numerous studies in the literature have applied DES to traditional manufacturing systems, yielding positive outcomes, especially in the areas of process scheduling and resource management [25]. For instance, [PERSON] used Plant Simulation to enhance the efficiency and resource utilization in warehouse order-picking operations [26]; [PERSON] et al. applied FlexSim to optimize AGV-based transport tasks within warehouse environments [27]; and [PERSON] et al.