# Dynamic Scheduling and Optimization of AGV in Factory Logistics Systems Based on Digital Twin
## 8 Conclusions  
In this paper, an AGVs multi-objective dynamic scheduling method (AMODS) based on digital twins in a factory logistics system is proposed. The proposed AMODS method is applied to the AGV dynamic scheduling problem of a digital twin system, and an application example is given. The main conclusions are as follows:  
1. In the traditional factory logistics system, the data of information space and physical space lack integration and interaction, and the whole process elements in the transportation process have low predictability, linkage and global optimization level. The scheduling framework based on digital twins is built, and the virtual digital twin is constructed to map the physical logistics system with data and model. Through the combination of data and model, the interaction of a virtual action model, simulation analysis model and physical equipment is achieved. 2. The real-time updated dynamic task list is introduced to generate a scheduling plan according to the actual production line demand. An AGVs multi-objective dynamic scheduling (AMODS) method based on digital twins was designed. The test results indicate that the time cost of the traditional scheduling is about 12 min, while the time cost of the real-time dynamic task list scheduling is about 10 min, which is 16.7% shorter than the traditional scheduling. The advantages of this method are verified and the transportation efficiency is improved. 3. The AGVs attributes are optimized with multi-level experiments. Through the case study, it proves the feasibility and good scheduling performance of the production line with the help of the AGVs multi-objective dynamic scheduling method (AMODS) based on the digital twin. The experimental results show that the optimal number of AGVs is three, the optimal no-load speed of AGVs is 1.5 m/s, and the optimal load speed of AGVs is 0.8 m/s. This provides a new solution for AGVs scheduling in a workshop logistics system. Based on the dynamic data exchange and fusion of the digital twin system, it can realize the synchronization of the digital and physical workshop and the demand prediction and optimal utilization of resources. However, the dynamic intelligent algorithm and predictive demand analysis studied in this paper are not comprehensive. Furthermore, it only considers the simulation development of the production line by understanding the working characteristics. At the same time, the production line layout and AGV routes of this study case are relatively simple, while the actual shop floor layouts are more complex. In the future, the scheduling and optimization based on artificial intelligent algorithms, AGV fault prediction and AGV power consumption prediction still need to studied further. They also need to act on a real workshop scene with a complex layout. S.W. : conceptualization, methodology, original draft, software and validation, project administration, funding acquisition. W.X. : writing--original draft preparation. W.L. : writing--review and editing. C.W. : investigation. L.C. : investigation. All authors have read and agreed to the published version of the manuscript. The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (No. 52005338) and National Natural Science Foundation of China (No. 51975444). Not applicable.