## 3 Driver-Inspection Vehicle Automatic Scheduling Method
### Dynamic Automatic Scheduling Algorithm Based on Multi-Objective Optimization
However, despite the significant performance improvements demonstrated by this method in experiments, there are still some limitations in practical applications. For example, the complexity of real customs environments may impose higher requirements on the algorithm's real-time capabilities, and for larger-scale scheduling problems, the algorithm's computational complexity may need further optimization. Additionally, the accuracy and robustness of biometric recognition in different environments may be affected by external factors such as lighting changes and environmental noise. Future research will focus on several key directions to build upon the findings. First, it aims to explore the incorporation of machine learning techniques into our scheduling framework to enhance predictive capabilities. By analyzing historical data, it can develop algorithms that anticipate vehicle arrivals and dynamically adjust schedules to optimize operations during peak traffic periods. Additionally, it plans to validate and test the proposed method in different operational contexts, including various customs environments and scenarios with fluctuating resource availability. Understanding how the method performs under diverse conditions will provide valuable insights into its robustness and adaptability. Furthermore, it proposes investigating new algorithms that could further enhance scheduling efficiency. For instance, hybrid approaches that combine genetic algorithms with other optimization techniques, such as reinforcement learning or simulated annealing, could yield even more effective solutions for complex scheduling problems. By pursuing these avenues, our future work aims to refine and expand the capabilities of automatic scheduling systems, ultimately contributing to more efficient and secure logistics operations across various domains. Overall, the proposed method for the automatic scheduling of customs inspection vehicles based on EVI and biometric recognition offers a novel solution for improving customs logistics management efficiency. Through the application of multi-objective optimization algorithms, this method successfully optimizes vehicle scheduling processes under multiple constraints. Future research will further expand the applications in this field and verify and refine this method in more complex real-world environments. Conceptualization, S.Z. and J.Y. ; methodology, J.X. ; software, Q.D. ; validation, Q.D., J.X. and S.Z. ; formal analysis, J.X. ; investigation, S.Z. ; resources, N.Z; data curation, N.Z. ; writing--original draft preparation, S.Z. ; writing--review and editing, Q.D. ; visualization, J.X. ; supervision, S.Z. ; project administration, J.Y. ; funding acquisition, N.Z. All authors have read and agreed to the published version of the manuscript. This Research was financially supported by the Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection (No. 2023B1212010011); Foshan Self-Funded Science and Technology Innovation Projects (Nos. 2320001007544, 2320001007511); Guangzhou Nansha District Innovation Team Project (No. 2021020 TD001); and the Guangzhou Key Research and Development Program (No. 2024B01W0002). No applicable. Author [PERSON] was employed by the company Guangzhou Haige Communications Group Incorporated Company and author [PERSON] was employed by the company Guangdong Zhongke Zhenheng Information Technology Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## References

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