## 1 Introduction  
With the continuous growth of global trade, customs authorities face increasing pressure to enhance the efficiency of vehicle inspection and management processes. Traditional scheduling methods often rely on manual operations and simplistic algorithms, leading to prolonged vehicle waiting times, inefficient resource allocation, and overall operational delays. As customs operations grow in complexity, innovative solutions are urgently needed to streamline these processes, resulting in long vehicle waiting times, low scheduling efficiency, and wasted resources. In recent years, To solve these problems, automatic scheduling technology and intelligent scheduling algorithms have been extensively studied and applied, providing new solutions for customs vehicle management [1; 2; 3]. However, most of these approaches remain in the simulation testing phase. The development of automatic scheduling methods has primarily focused on optimizing scheduling algorithms and improving scheduling efficiency. Traditional methods, such as First-Come, First-Served (FCFS) and heuristic algorithms, have been gradually replaced by automated scheduling approaches utilizing artificial intelligence and machine learning technologies [4; 5; 6]. Recent advancements in vehicle scheduling, particularly those incorporating deep reinforcement learning, have significantly enhanced scheduling efficiency and system adaptability, although they remain constrained by the operational rules of the research subjects [7]. Additionally, multi-objective optimization algorithms, such as Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), have been widely used in complex vehicle scheduling problems [8; 9; 10]. These algorithms optimize scheduling optimization by comprehensively considering various constraints (e.g., vehicle arrival time, priority, yard capacity), significantly improving scheduling efficiency and reducing both vehicle waiting time and yard resource occupancy. Automatic scheduling is crucial in addressing these challenges. By leveraging advanced technologies, customs can optimize vehicle relocation and inspection processes, thereby significantly reducing waiting times and enhancing resource utilization. The integration of Vehicle Electronic Identification (EVI) and biometric recognition technologies represents a novel approach to achieving these objectives. This method not only enhances the accuracy of vehicle identification but also improves security by ensuring that only authorized personnel can operate specific vehicles. Significant progress has been made in automatic scheduling technology for customs vehicle relocation. A blockchain-based EVI solution has been proposed to enhance the security and transparency of vehicle identification systems [11]. Scheduling methods that combine EVI technology with biometric recognition technology have emerged as a research hotspot. EVI technology enables real-time vehicle identification and positioning via RFID or OBU, while biometric recognition technology is used for driver identity verification and access management [12; 13; 14]. The synergy of these technologies improves the automation in vehicle scheduling and enhances the security and reliability of the management system. In practice, many customs and logistics parks have gradually implemented automatic scheduling systems to address increasingly complex vehicle scheduling needs. For instance, intelligent scheduling systems based on EVI and biometric recognition technology have demonstrated effectiveness at several large ports, effectively reducing manual intervention and improving scheduling accuracy [15; 16]. These systems automate scheduling processes, enhance accuracy, and effectively manage high volumes of vehicle entry and exit during peak periods. Research on automatic scheduling methods for customs vehicle relocation has increasingly focused on effectively combining EVI technology, biometric recognition technology, and multi-objective optimization algorithms [17; 18; 19]. [PERSON] et al.