# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 1 Introduction  
AGV scheduling is a crucial process in manufacturing systems [1, 2]. It aims at the allocation and timing management of transportation tasks to achieve goals such as reduction in completion time, energy consumption, and resource idle rate [3, 4]. In recent years, AGV scheduling optimization has been continuously improved with the development of advanced methods, such as the math programming method [5], the agent-based method [6], the machine learning method [7], etc. Although these novel methods have been involved in the scheduling, the accurate description of AGV property, such as the charging process, is always ignored. However, charging process of the AGV may take several hours, which may result in serious interruption and delay of the transport tasks. So, charging behavior should be carefully considered, otherwise, the scheduling cannot be well optimized. AGVs are normally powered by the batteries. In the actual shop flow, the AGV will break the task when the state of charge (SOC) of the battery reaches a certain threshold [8, 9]. Consequently, the AGV should be charged autonomously in the nearest charging area. However, this charging mode for AGVs may be harmful to the overall performance of the system [10]. When the battery power of the an AGV drops below the threshold, the current position of the AGV can, by chance, be far away from the charging area. Charging in advance at charging area closer to the initial position could be a better decision. This would decrease the total extra travel time from the work area to the charging area. Furthermore,the AGVs are normally fully charged with long charging time. Charging a portion of the AGV may be more reasonable when a rush transportation task is needed. Therefore, in addition to a module for allocating transport tasks, a battery management module for allocating charging tasks should also be included for a complete AGV scheduling system [11; 12]. Research in the field of AGV scheduling has begun to pay attention to battery management in recent years. [PERSON] et al. [13] explored the production capacity of the workshop through battery management. The results showed that more frequent charging of AGVs increases the productivity of the manufacturing system significantly. [PERSON] et al. [10] proposed an AGV scheduling method based on the TSP (Traveling Salesman Problem) model, which acquires the optimal charging timing and time to reduce the task completion time. These studies can reduce the impact of the charging process on the execution of AGV tasks with static scheduling methods. However, scheduling systems using the static method has limited performance in a real production environment, since the status of a real production environment is changeable. In order to improve the adaptability of the scheduling system to the environment, dynamic scheduling methods are studied. [PERSON] et al. [4] proposed a scheduling method based on dynamic priority, which becomes the commonly used AGV dynamic scheduling method. [PERSON] et al. [6] proposed an agent-based dynamic scheduling method. Each agent in the system was autonomous and could negotiate and cooperate with other agents. [PERSON] et al. [14] proposed a dynamic planning method for AGV based on the time window. However, these research works have some common shortcomings. Firstly, with a predefined mathematical model, these solutions cannot possibly describe the status of the AGV accurately and dynamically. Secondly, these methods cannot predict the AGV's availability for a period of time, so a global optimal scheduling plan cannot be obtained. Hence, for the AGV scheduling considering battery management, there are still many limitations if the traditional dynamic scheduling methods are used. Digital twin has been widely used in manufacturing in recent years [15; 16], providing a new solution to the scheduling problem of the workshop. [PERSON] et al.