## 3 Driver-Inspection Vehicle Automatic Scheduling Method
### Dynamic Automatic Scheduling Algorithm Based on Multi-Objective Optimization
### Dataset

To verify the effectiveness of the proposed method, experiments were conducted within a simulated customs logistics scenario. The scenario encompasses multiple vehicles waiting for inspection, alongside yards characterized by various resource constraints such as yard capacity and vehicle type restrictions, as well as dynamic factors, including vehicle arrival times and driver statuses. Vehicle identification is facilitated through EVI technologies, including Radio Frequency Identification (RFID) and On-Board Units (OBU), ensuring real-time vehicle tracking and positioning. Driver identity verification employs multi-modal biometric recognition methods, including facial, fingerprint, and behavior recognition, to ensure accurate identity verification authority management. The dataset is sourced from the actual operational records of Xinsha Port at Huangpu Customs, including vehicle arrival times, vehicle types, yard information, inspection requirements, and driver information, which provides a comprehensive representation of vehicle movements and inspection processes. It utilized a dataset derived from actual customs operations, capturing vehicle movements and inspection processes over the period of 2020-2023. This dataset serves as a comprehensive representation of the dynamic nature of customs vehicle management. The dataset stored 2,879,256 historical records, which provides detailed records of vehicle entries, inspection requirements, and processing times. This real-world dataset ensures that our findings are grounded in practical applications. The dataset includes several important features: vehicle types (various categories such as passenger cars, trucks, and specialized vehicles, each with distinct inspection requirements that influence the scheduling process), arrival patterns (detailed records of vehicle arrivals, highlighting peak and off-peak periods, which are critical for evaluating scheduling efficiency and understanding operational challenges), and inspection requirements (information on different inspection protocols that must be followed based on vehicle type and cargo, providing insight into how these requirements impact overall processing times). The dataset is unique in that it encompasses diverse operational conditions, including the following: variability in traffic volume (the dataset captures variations in traffic volume during different times of the day and week, enabling a thorough analysis of how scheduling strategies can be adapted to changing conditions), resource availability (the dataset records fluctuations in resource availability, such as the number of inspection lanes and available personnel, allowing us to assess how resource constraints affect scheduling decisions), and domain context (this dataset is particularly relevant to customs logistics management, as it reflects the complexities and challenges faced by customs authorities in real-world scenarios. By analyzing these data, we aim to develop solutions that enhance operational efficiency and improve the overall effectiveness of customs inspections. The insights gained from this dataset will inform the algorithm's design, ensuring that it is well-suited to address the specific needs of customs operations). To simulate various scheduling scenarios, multiple datasets were generated to assess the algorithm's performance under different conditions. These datasets included driver authorization through multi-modal biometric data, comprising facial, fingerprint, and behavioral data, which were used to train deep learning models for efficient driver identity verification and management. Independent variables of the experiment included vehicle arrival time, vehicle type, yard capacity, inspection requirements, and driver workload. Evaluation indicators comprised vehicle waiting time, relocation path length, resource utilization, and driver workload balance, among others.