## 5 Experimental Simulation and Result Analysis
### Comparative Analysis of Algorithms  
Based on the aforementioned case study, a comparative analysis of the proposed greedy strategy state transition algorithm and the genetic algorithm is conducted. The superiority of the algorithm presented in this paper is verified through the obtained objective function scores, the average number of iterations, and the average iteration time. Since the crossover operator in the classical genetic algorithm involves partial sequence exchanges between multiple individuals, and the mutation operator may cause changes to the individual vehicle's characteristics (such as vehicle type and drive mode), it alters the original vehicle dispatch sequence in the paint shop. Therefore, when comparing the algorithms, this paper makes some improvements to the classical genetic algorithm by redesigning the mutation and crossover genetic operators as swap operators. The specific operation of the swap operator is to select two vehicles in the overall vehicle sequence and exchange their positions, thus avoiding violations of vehicle type quantity constraints and vehicle number constraints during the calculation process. \begin{table}
\begin{tabular}{c c c c} \hline
2\(\rightarrow\)5 & 5\(\rightarrow\)14 & 13\(\rightarrow\)20 & 17\(\rightarrow\)26 \\ \hline
31\(\rightarrow\)34 & 44\(\rightarrow\)51 & 46\(\rightarrow\)55 & 53\(\rightarrow\)58 \\
62\(\rightarrow\)70 & 66\(\rightarrow\)85 & 71\(\rightarrow\)80 & 73\(\rightarrow\)92 \\
76\(\rightarrow\)88 & 100\(\rightarrow\)106 & 102\(\rightarrow\)111 & 118\(\rightarrow\)126 \\
121\(\rightarrow\)129 & 134\(\rightarrow\)143 & 154\(\rightarrow\)163 & 155\(\rightarrow\)164 \\
159\(\rightarrow\)168 & 172\(\rightarrow\)181 & 177\(\rightarrow\)186 & 193\(\rightarrow\)202 \\
196\(\rightarrow\)205 & 219\(\rightarrow\)235 & 223\(\rightarrow\)232 & 230\(\rightarrow\)239 \\
244\(\rightarrow\)252 & 268\(\rightarrow\)270 & 272\(\rightarrow\)283 & 276\(\rightarrow\)284 \\
278\(\rightarrow\)287 & 281\(\rightarrow\)290 & 287\(\rightarrow\)296 & 297\(\rightarrow\)309 \\
304\(\rightarrow\)312 & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Table of the movement of the painting production line sequence for dataset R in the PBS scheduling system. Figure 10: Iteration plot for the state transition algorithm based on the greedy strategy for dataset R.  
By using Matlab programming, the objective function scores, average number of iterations, and average iteration time for dataset N and dataset R can be calculated under both the genetic algorithm and the greedy strategy state transition algorithm. The results are shown in Table 3.  
As shown in Table 3, regardless of whether it is the normal dataset N obtained after desensitizing production data or the adjusted dataset R designed for testing the adaptability of the model and algorithm, the objective function scores obtained by the algorithm proposed in this paper are higher than those of the genetic algorithm. The genetic algorithm has a smaller average number of iterations compared to the algorithm presented in this paper. Based on the objective function scores and the number of iterations, it can be inferred that the genetic algorithm eventually falls into a local optimal solution, while the greedy strategy state transition algorithm is closer to the global optimal solution. The iteration diagrams for dataset N and dataset R under different algorithms are shown in Figure 11, which provides an intuitive view of the average number of iterations and the objective function scores. Therefore, based on the comparison of the results, it can be concluded that the algorithm proposed in this paper performs better overall than the classical genetic algorithm. Figure 11: Iterative diagrams of dataset N and dataset R under different algorithms. (**a**) The average number of iterations for solving dataset N using different algorithms. (**b**) The average number of iterations for solving dataset R using different algorithms.