## 3 Driver-Inspection Vehicle Automatic Scheduling Method
### Dynamic Automatic Scheduling Algorithm Based on Multi-Objective Optimization
This consistency is crucial for customs operations, as it ensures predictability in scheduling results when responding to varying traffic levels and changes, thereby enhancing overall operational efficiency. The experimental results indicate that the proposed multi-objective optimization algorithm outperforms NSGA-II and MOEA/D in terms of computational complexity, particularly in large-scale vehicle scheduling scenarios, demonstrating higher efficiency and stability. This demonstrates that the algorithm not only excels in computation time but can also be effectively implemented in resource-constrained real-world environments. The proposed method exhibits strong robustness in handling scheduling problems in different scenarios. Notably, in conditions of peak traffic flow and tight resources, the optimization algorithm can adaptively adjust scheduling schemes to maintain scheduling effectiveness while maximizing resource utilization. Even in scenarios characterized by high traffic demands, the proposed algorithm maintains stability, ensuring that the computational complexity remains within an acceptable range when tackling large-scale scheduling challenges. Overall, the results validate the effectiveness of the proposed multi-objective optimization algorithm in enhancing the automatic scheduling of customs inspection vehicles, underscoring its potential for practical applications in dynamic and complex environments. ## 5 Conclusions and Outlook

This study presents an innovative automatic scheduling method for relocating customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. Our findings demonstrate that the proposed method significantly enhances the efficiency of customs logistics management, achieving a reduction in average vehicle waiting times by approximately 30% and an increase in resource utilization rates by around 25%. These improvements underscore the potential of integrating advanced technologies into the scheduling process, thereby addressing the critical challenges faced by customs authorities. The implications of the research extend beyond the immediate context of customs operations. By streamlining vehicle management processes, the method contributes to more efficient trade logistics, potentially leading to reduced operational costs and improved service delivery. The success of the approach indicates that similar methodologies could be adapted for use in various logistical environments, promoting broader applications of automatic scheduling technologies. The main technical contributions of this study include the following:

**The integration of Vehicle Electronic Identification and biometric recognition technologies:** the use of RFID technology along with three biometric recognition techniques (facial, fingerprint, and behavior recognition) successfully achieves efficient matching between vehicle and driver identities, thereby enhancing the accuracy and security of customs vehicle scheduling. **The application of a multi-objective optimization algorithm:** a multi-objective optimization algorithm is designed and implemented, taking into account vehicle waiting time, relocation path length, resource utilization, and driver workload balance. This algorithm effectively improves the overall performance of vehicle scheduling. **Multi-modal feature fusion:** for the biometric recognition process, this study adopts deep learning methods for multi-modal feature fusion, leading to more accurate and robust driver identity verification in complex environments. Through simulation experiments and validation with real data, the proposed automatic scheduling method demonstrates superior performance in various evaluation metrics, particularly in reducing vehicle waiting times, optimizing resource allocation, and balancing driver workloads during peak periods. The method shows strong robustness and adaptability.