## 3 Driver-Inspection Vehicle Automatic Scheduling Method
### Dynamic Automatic Scheduling Algorithm Based on Multi-Objective Optimization
Pareto front update: All non-dominated solutions are retained as Pareto optimal solutions, and dominated solutions are discarded. This ensures that the final solution set contains a diversified array of optimal schemes. Specifically, individuals with the lowest non-dominance rank and the highest crowding distance are selected to form a new population for the next iteration. By integrating multi-objective optimization and Pareto selection, the algorithm generates a diversified Pareto front solution set, providing decision-makers with a variety of options based on actual needs. **Step 5: Termination condition judgment**. The algorithm assesses termination conditions to determine when to stop the optimization process. Termination occurs when either the preset number of iterations is reached or when the convergence criteria are satisfied. Upon meeting these conditions, the final Pareto optimal solution set is generated and outputted for further analysis and decision-making [43]. The algorithm continually checks whether the termination conditions are met, including reaching the maximum number of iterations or satisfying the convergence criteria for the Pareto front. If these conditions are fulfilled, the final Pareto front solution set is output; otherwise, the iteration process continues until one of the conditions is met. A pseudocode of the proposed algorithms is shown as follows (Algorithm 1):  
``` Input: -Vehicle data (EVI, biometric recognition information) -Custom inspection criteria -Scheduling constraints -Vehicle attributes (type, priority, et al.) -Resource availability (inspectors, equipment, et al.) Output: -Optimized vehicle scheduling scheme
1. Initialize scheduling parameters
2. Define multi-objective functions: 2.1 Minimize total wait time 2.2 Minimize inspection cost 2.3 Maximize resource utilization 2.4 Minimize vehicle downtime 2.5 Minimize the number of inspections per inspector The convergence of the multi-objective optimization algorithm is defined based on the following criteria:

Objective function stability: Monitoring the values of the objective functions over successive iterations. The algorithm is considered to have converged when the improvement in these objective function values falls below a specified threshold (1% change over 10 consecutive iterations). This indicates that the algorithm is no longer making significant progress toward better solutions. Pareto front consistency: In multi-objective optimization, convergence is also assessed by examining the stability of the Pareto front. If the set of non-dominated solutions remains stable and exhibits minimal variation over several generations, it will think that the algorithm has reached convergence. Iteration limit: Additionally, a maximum number of iterations for the algorithm will be imposed. If this limit is reached, the algorithm terminates, and the current best solutions are returned, even if the convergence criteria have not been fully met. Performance metrics: Evaluating convergence not only by the stability of the objective functions and Pareto front but also by tracking key performance metrics such as computational time and resource utilization. A balance between these metrics and convergence indicators is essential to ensure the efficiency of the scheduling solution. Implementing these criteria can ensure that our algorithm effectively approaches optimal solutions while balancing the trade-offs inherent in multi-objective optimization. ## 4 Experiments and Analysis

This section presents the experimental design, results, and analysis of the automatic scheduling method for relocating customs inspection vehicles based on Vehicle Electronic Identification (EVI) and biometric recognition. The experiments integrate simulation and real data to assess the effectiveness of the proposed multi-objective optimization scheduling algorithm, focusing on metrics such as vehicle scheduling efficiency, resource utilization, driver workload balance, and other aspects.