## 3 Driver-Inspection Vehicle Automatic Scheduling Method
### Dynamic Automatic Scheduling Algorithm Based on Multi-Objective Optimization
### Experiments and Application Effect Analysis

After preprocessing the original data obtained from customs, including normalizing EVI data and biometric data, the parameters of the multi-objective optimization algorithm were initialized (e.g., population size, number of iterations, objective weights), and the multi-objective optimization algorithm, based on improved PSO, was applied for dynamic scheduling. The algorithm began by generating an initial population, then iteratively optimizes through the multi-objective evolutionary process to produce the final Paretooptimal solution set. During each iteration, objective function values were calculated based on the requirements of current scenario, which included vehicle waiting time, relocation path length, resource utilization, driver workload balance, relocation time, scheduling uncertainty, vehicle scheduling consistency, system throughput, and other indicators. The population was updated according to the selection strategy. Experimental results, including the values of the objective functions for each generation during the optimization process and the final Pareto front solution set, were collected. These results were compared with benchmark methods to validate the effectiveness of the improved algorithm. Figure 2 illustrates the Pareto front solution sets under different objective combinations. The results indicate that the proposed multi-objective optimization algorithm can effectively balance multiple objectives. For instance, solutions aimed at reducing vehicle waiting time often resulted in an increase in relocation path length, highlighting the trade-offs between resource utilization and driver workload balance. Table 1 shows the results for eight objective indicators in two scenarios based on real business data from a working day in August 2024. It should be noted that the data in the table are the average values of all complete business data for the day, and they have been compared and validated against benchmark methods (manual scheduling algorithms and simple heuristic algorithms). The distribution of the Pareto front shows that the proposed method achieves a good balance among multiple objectives. The proposed method outperforms the benchmark methods in vehicle waiting time and relocation path length, while also showing excellent performance in resource utilization and driver workload balance. In some solution sets, vehicle waiting time and relocation path length are significantly optimized, while in others, resource utilization and driver workload achieve better balance. These results indicate that the proposed algorithm can flexibly adjust according to actual needs to meet different scheduling requirements. Figure 2: Pareto front solution. (**a**) Pareto Front Solution between Vehicle Waiting Time and Relocation Path Length; (**b**) Pareto Front Solution between Balancing Driver Workload and Relocation Path Length; (**c**) Pareto Front Solution between Resource Utilization Rate and Relocation Path Length; (**d**) Pareto Front Solution between Resource Utilization Rate, Vehicle Waiting Time, and Relocation Path Length; (**e**) Pareto Front Solution between Balancing Driver Workload, Vehicle Waiting Time, and Relocation Path Length; (**f**) Pareto Front Solution between Balancing Driver Workload, Vehicle Waiting Time, and Resource Utilization Rate. ### Comparison of Algorithm Performance and Analysis

To verify the effectiveness of the proposed multi-objective optimization algorithm for the automatic scheduling method of relocating customs inspection vehicles based on EVI and biometric recognition, a comparative analysis of the convergence performance of various algorithms was conducted. The experimental results demonstrated that, with an increasing number of generations, the multi-objective optimization algorithm converged rapidly, successfully identifying solutions that closely approximated the Pareto optimal set in a short time frame.