## 3 Driver-Inspection Vehicle Automatic Scheduling Method
### Dynamic Automatic Scheduling Algorithm Based on Multi-Objective Optimization
This improvement is primarily due to the use of Vehicle Electronic Identification and biometric recognition technologies, which enable real-time monitoring and scheduling of vehicles awaiting inspection, thus reducing the delays associated with traditional methods that rely on manual judgment. The effective dynamic scheduling mechanism allows vehicles to swiftly enter the inspection process based on priority, optimizing customs operational efficiency. The reduction in relocation path length indicates that the proposed method not only considers the arrival times of vehicles but also effectively utilizes information from the environment, such as real-time traffic conditions and vehicle locations. This information-based path planning approach reduces unnecessary driving distances and enhances overall logistical efficiency. The resource utilization rate reaches 93.34%, while NSGA-II and MOEA/D only achieve 85.25% and 88.33%, respectively. This result demonstrates that through intelligent scheduling algorithms, customs can maximize the use of available resources during peak times, ensuring the efficient operation of inspection personnel and equipment. High resource utilization also reflects the flexibility and adaptability of the new method during the scheduling process, effectively reducing resource idling. The significant improvement in driver workload balance (0.72) indicates that the proposed method comprehensively considers drivers' working hours and rest periods during scheduling. This not only enhances drivers' job satisfaction but also reduces potential safety hazards caused by excessive fatigue, further improving the overall safety of customs operations. The reduction in scheduling uncertainty metrics shows the proposed method's flexibility in responding to unexpected events. The dynamic scheduling mechanism can adjust scheduling strategies in response to fluctuations in vehicle traffic, effectively handling increases in traffic during peak times and unforeseen events, thereby enhancing the system's robustness. The increase in system throughput (reaching 408.82 vehicles/h) validates the proposed method's capability to operate efficiently. This result indicates that in a high-traffic customs environment, the new method can effectively increase the rate of vehicle processing, thereby supporting enhanced trade efficiency. The reduction in relocation time (10.5 min) reflects the optimization of the scheduling process, ensuring that vehicles can quickly complete relocation operations and minimizing time wasted due to waiting in queues. By comprehensively considering arrival times, priorities, and other constraints, the algorithm achieves more efficient scheduling. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Indicators** & **Automatic Scheduling Method** & **NSGA-II** & **MOEA/D** \\ \hline Vehicle Waiting Time (min) & 14.30 \(\pm\) 1.81 & 16.71 \(\pm\) 2.58 & 18.43 \(\pm\) 2.32 \\ \hline Relocation Path Length (m) & 1048.41 \(\pm\) 323.25 & 1182. \(\pm\) 392.09 & 1201.65 \(\pm\) 401.07 \\ \hline Resource Utilization (\%) & 93.34 \(\pm\) 1.51 & 85.25 \(\pm\) 2.12 & 88.33 \(\pm\) 2.70 \\ \hline Driver Workload Balance (h) * & 0.72 \(\pm\) 0.05 & 0.77 \(\pm\) 0.06 & 0.75 \(\pm\) 0.04 \\ \hline Relocation Time (min) & 21.31 \(\pm\) 4.22 & 23.18 \(\pm\) 5.03 & 24.15 \(\pm\) 6.02 \\ \hline System Throughput (/h) & 408.82 \(\pm\) 10.68 & 375.45 \(\pm\) 12.8 & 357.58 \(\pm\) 13.03 \\ \hline Scheduling Uncertainty (min) & 10.5 \(\pm\) 1.13 & 12.25 \(\pm\) 1.44 & 13.98 \(\pm\) 1.29 \\ \hline Vehicle Scheduling Consistency (min) & 10.93 \(\pm\) 0.88 & 12.75 \(\pm\) 0.94 & 12.14 \(\pm\) 1.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of different algorithms on various performance indicators. The decrease in the scheduling consistency objective function (10.93) implies that the new method can maintain stable scheduling outcomes under different operational conditions.