## 3 Driver-Inspection Vehicle Automatic Scheduling Method  
To enhance the efficiency of relocation operations, this study proposes an automatic scheduling method utilizing Vehicle Electronic Identification (EVI) and biometric recognition. This method comprehensively considers the driver's authority, vehicle status, and real-time environmental data to achieve optimal scheduling. The main steps are as follows:  
**Step 1: Driver identity verification and authority confirmation.**  
Biometric recognition technology is employed to verify the driver's identity, which is then matched with the corresponding vehicle information. Only verified drivers are granted the authority to operate specific vehicles. This step ensures the safety and legality of the association between drivers and vehicles.  
**Step 2: Identification and path confirmation of inspection vehicles.**  
Through EVI and roadside equipment, vehicles awaiting inspection are assigned a temporary identity unique to the authorized timeframe. The optimal relocation path is determined to effectively arrange and utilize the space in the transit area.  
**Step 3: Combining vehicle status and scheduling requirements.**  
Once identity verification is completed, the system dynamically matches drivers with vehicles based on their authority and vehicle status to ensure optimal relocation operations. The vehicle status information provided by EVI (e.g., vehicle location, current task status) is integrated with the driver's identity and behavior data to generate a dynamic scheduling plan. This study proposes a multi-objective optimization-based scheduling algorithm that comprehensively considers the following factors:  
1. Priority of vehicles waiting for inspection: the system automatically generates vehicle priorities based on customs inspection requirements, arrival times, and vehicle types, matching them with suitable drivers.
2. Driver idle time and skill matching: the relationship between drivers and vehicles is dynamically adjusted based on the driver's current status, skill level, and historical operation records to ensure optimal resource allocation.
3. Path optimization: utilizing genetic algorithms, dynamic planning of relocation paths minimizes the distance and time required for vehicle movement, thereby enhancing overall scheduling efficiency.  
**Step 4: Dynamic scheduling.**  
By thoroughly analyzing the vehicle priorities, current yard status, and the driver's capabilities, the scheduling plan is dynamically adjusted to optimize the relocation paths and reduce waiting times. The scheduling scheme is modified in real-time using multi-objective optimization algorithms. The scheduling scheme considers not only the vehicle's status and location but also the driver's current task status and operational capability, ensuring both efficiency and safety in scheduling. During execution, the system continuously monitors the status of drivers and vehicles to facilitate smooth implementation of the scheduling scheme. In the event of anomalies (e.g., abnormal driver status or changes in vehicle status), information fusion and scheduling adjustments are conducted in real-time. The global view of the proposed solution is shown in Figure 1.