# Dynamic Scheduling and Optimization of AGV in Factory Logistics Systems Based on Digital Twin
## 2 Related Works  
In a logistics system, the path planning and scheduling strategy of AGV is the core of the whole system, and the quality of the scheduling algorithm can directly affect the balance and stability of the system. Multi-AGV path planning has always been a hot research topic, and many researchers have improved the scheduling strategy of AGVs. [PERSON] et al. [7] considered the constraints of transportation time and transportation resources in the flexible job shop and proposed an improved local search algorithm combining greedy heuristic rules. [PERSON] et al. [8] proposed a hybrid algorithm of democratic robotics PSO and improved Q-learning, and found that it is fast and available for a real-time environment. [PERSON] et al. [9] studied the multi-objective path planning problem and found the shortest collision-free path connecting a set of given target points in the robot's working environment. [PERSON] et al. [10] proposed a dynamic scheduling method for self-organizing AGVs in a logistics system. Dynamic scheduling rules are learned by using improved gene expression programming and cooperate with other AGVs for transportation. [PERSON] et al. [11] realized integrated scheduling and conflict-free path planning for multiple AGVs. By establishing a mixed integer programming model and using hybrid genetic algorithm-particle swarm optimization (HGA-PSO), AGV path conflict or deadlock is avoided. [PERSON] et al. [12] proposed a new variable neighborhood search algorithm for the conflict-free scheduling of large-scale multi-load AGVs. The retention time table is used to prevent collisions and deadlocks between multiple AGVs, which can achieve the scheduling of multiple AGVs in a large and dense network. [PERSON] et al. [13] proposed a new multi-state scheduling algorithm (MSSA) to make use of idle AGVs. [PERSON] et al. [14] took minimizing the completion time as the goal, analyzed the load time and no-load time of each station, and proposed an improved gray wolf algorithm to optimize the scheduling of AGVs in a flexible job-shop. In addition to the design of AGVs scheduling strategy, most flexible job-shop logistics systems are based on the premise of multi-target objects. In view of the attributes of AGVs itself, [PERSON] et al. [15] proposed a multi-AGVs efficient routing combining centralized control and decentralized control. Centralized control optimized the AGV path nodes, while decentralized control adjusted the AGV speed. [PERSON] et al. [16] proposed a throughput evaluation methodology based on AGV route decomposition that is proposed for AGV transportation systems. AGV speed change and collision avoidance are considered, as well as the promotion of the productivity of AGV transportation system. [PERSON] et al. [17] proposed an efficient Multi-Objective Greed Algorithm (MOGA). The algorithm has effective strategies such as new population initialization, greedy operation, and an adaptive multi-domain local search, and optimizes the energy consumption of AGVs and the number of AGVs. For the adaptability of the scheduling system to the real environment, the application of dynamic scheduling in the actual discrete shop floor has always been a difficult problem. [PERSON] et al. [18] constructed the mathematical model of flexible multi-objective dynamic scheduling of the shop floor and proposed a multi-objective flexible dynamic scheduling algorithm based on an adaptive genetic algorithm. [PERSON] et al. [19] combined a dynamic interaction layer and particle swarm genetic hybrid algorithm to realize the dynamic scheduling of urgent tasks in view of the dynamic disturbance in flexible job shop scheduling. For the urgent orders for small batch production tasks, [PERSON] et al. [20] proposed a double-loop deep Q network based on a perception-cognition dual system to minimize the average load of equipment and to minimize the total completion time. In reality, the power of AGVs is limited, and working for a period of time will consume the battery power.