## 3 Model Establishment
### The Method of Modelling Process
The Plant Simulation software used in this study, as shown in Figure 5, builds a comprehensive workshop simulation model, including assembly stations, material flow paths, and AGV routes to replicate the production process. By configuring key parameters such as workstation processing times and AGV operating speed parameters, the model effectively mirrors real workshop conditions. During the simulation, real-time data on task execution and idle times at each station are collected, enabling bottleneck identification and supporting resource allocation optimization. This provides a solid foundation for process improvement. To ensure the accuracy of the simulation results, several key parameters were defined in the model, as summarized in Table 1. The workshop includes 11 workstations, covering essential stages from part storage and segmented assembly to final packaging. The processing time for each assembly operation ranges from 20 to 35 min, with an average of approximately 28 min, based on actual process data provided by the enterprise. AGVs serve as the primary means of material transport, with their quantity set between one and three units. Each AGV has a maximum operating speed of 1.2 m/s, and both acceleration and deceleration are set at 0.3 m/s2. The loading and unloading time per AGV is configured at 45 s. To account for energy constraints, each AGV is equipped with a battery capacity that supports continuous operation for up to 3 h. When the battery level falls below a designated threshold, the system automatically initiates a return-to-charge protocol. To clearly define the optimization objectives and ensure the comparability of the results, workstation utilization and the number of rework items were established as KPIs. Each simulation cycle was set to 8 h per day, and every configuration scenario was independently executed 30 times. The average values were then calculated to minimize random errors, enhance result stability, and reduce the likelihood of being affected by local optima. ### Simulation Modelling and Optimization

The initial simulation results indicated notable discrepancies in workstation task durations and insufficient coordination among stations, which led to low utilization rates. To address these issues and improve efficiency, the optimization strategy involved increasing the number of AGVs responsible for product transport from one to three while maintaining two AGVs for material delivery, along with the implementation of workstation buffers. After optimization, workstation utilization rose to over 26%, significantly enhancing the overall efficiency of the assembly line. ## 4 Experimental Results and Analysis

To ensure the reliability of the experimental results, the experiments were carefully designed with close attention to key variables. Critical factors such as the number of workstations and AGVs were determined based on the specific task distribution within the workshop. A simulation model was developed using Plant Simulation software to \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Average processing time per assembly operation & 20–35 min \\ Number of workstations & 11 \\ Number of AGVs & 5 \\ Maximum AGV speed & 1.2 m/s \\ Acceleration/Deceleration & 0.3 m/s\({}^{2}\) \\ Loading/Unloading time & 45 s \\ AGV battery capacity & 3 h \\ AGV Load Capacity & 800 kg \\ Number of simulation runs & 30 independent runs \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The relevant parameters in the model. Figure 5: Aerospace workshop simulation model. accurately replicate real-world factory conditions. During the simulation process, real-time data on task execution and workstation idle times were collected to evaluate system performance. Based on this preliminary data, optimization strategies including increasing the number of AGVs and rebalancing workloads among workstations were implemented to improve overall operational efficiency.