## 6 Experimental Comparison and Analysis
### Comparison of Heuristic Algorithms  
Evaluation of the performance of the nine heuristic algorithm strategies was conducted by comparing the relevant data for each strategy. Figure 3 presents the Average Percentage Relative Deviation (APRD) for all strategies. The results indicate that C1S3 exhibits the largest fluctuations, with a deviation span of approximately 8. In terms of numerical values, all strategies are closely clustered around 2, with C2S2 showing the smallest deviation and C1S2 showing the largest. Figure 3: APRD value distribution under each strategy. Table 1 provides a more detailed description of the nine strategies, including both APRD and Computation Time (CT). The data presented were obtained from multiple experiments, comparing each strategy under different combinations of workpiece quantity (\(n\)), machine quantity (\(m\)), and AGV quantity (\(a\)). The table shows that CT increases as \(n\), \(m\), and \(a\) rise, particularly when \(n=100\), where CT reaches a maximum of 1129.22 s, significantly higher than the 238.42 s observed when \(n=20\). This indicates that an increase in the number of workpieces, machines, and AGVs directly affects the complexity of the scheduling problem, resulting in a substantial rise in computation time. Overall, the data presented in the table highlight the strengths and weaknesses of the various heuristic strategies across different configurations. C2S2 consistently outperforms C1S1 in terms of APRD, especially when \(n=3\) and \(a=6\), where it approaches the optimal solution more closely. Within the same AGV selection strategy, changes in the AGV standby strategy led to an increase in APRD, suggesting that different standby strategies have a direct impact on the algorithm's scheduling accuracy. The average APRD values for the combinations of \(n\), \(m\), and \(a\) across the strategies C1S1, C1S2, C1S3, C2S1, C2S2, C2S3, C3S1, C3S2, and C3S3 are as follows: 22.57%, 24.20%, 23.91%, 23.48%, 16.44%, 20.34%, 16.78%, 18.62%, 22.95%, 29.71%, 30.50%, 33.50%, 31.31%, 19.54%, 27.64%, 19.67%, 23.81%, 30.41%, 30.09%, 32.26%, 31.88%, 31.31%, 21.92%, 27.12%, 22.38%, 24.82%, and 30.60%. Based on the average values across the strategies, C2S2 performs the best, followed by C3S1, while C1S2 and C1S3 perform comparatively poorly. To further analyze the impact of different AGV selection strategies under varying operational constraints, we examined their performance across different job sizes, machine configurations, and AGV quantities. The results indicate that the choice of selection strategy significantly influences scheduling efficiency, particularly in scenarios with high job arrival rates or limited AGV availability. For instance, the Nearby Choice (NC) strategy exhibited superior performance in minimizing idle time when AGVs were scarce, whereas the Sequential Choice (SC) strategy provided more stable performance across different workload intensities. These findings highlight the importance of selecting an appropriate AGV scheduling strategy based on specific operational constraints. Future work could extend this analysis by incorporating additional real-time constraints such as AGV battery levels and dynamic traffic conditions. To further evaluate the robustness of the proposed AGV scheduling strategies, we conducted a sensitivity analysis under varying operational conditions. Specifically, we analyzed the impact of different job sizes (\(n=\) [20, 30, 50, 100]), machine configurations (\(m=\) {4, 6, 8}), and AGV quantities (\(a=\) {4, 6, 8}) on scheduling performance. The results