# A Method for Dynamic Insertion Order Scheduling in Flexible Job Shops Based on Digital Twins
###### Abstract  
Various production disturbances occurring in the flexible job shop production process may affect the production of the workshop, some of which may lead to the prolongation of production completion time. Therefore, a flexible job shop dynamic scheduling method based on digital twins is proposed and a dynamic scheduling framework is constructed. Compared with the traditional workshop, the digital twin-based flexible job shop can upload the relevant production data of the physical workshop to the data management center in real time, and after fusion processing the data can work cooperatively with the upper application system. Taking the dynamic disturbance of rush order insertion as an example, the dynamic scheduling of insertion order is realized based on the dynamic scheduling framework, and then the production efficiency is improved. To achieve the shortest completion time, a mathematical model for dynamic scheduling optimization is established and a genetic algorithm (GA) is applied to solve the model. Finally, a practical case is applied to show that the completion time of this algorithm is reduced by 35%, which verifies the feasibility of the  
and achieved better results. [PERSON] et al. [12] artificially set the global disturbance capacity of the disturbance factors and established a dynamic scheduling strategy for the affected workpiece. [PERSON] et al. [13] studied the flexible job shop scheduling problem with machine faults, constructed relevant constraint models including aspects of the spare material condition and the machine state, and proposed different strategies for insertion rescheduling and full rescheduling.  
In summary, the current research on the FJSP mainly focuses on a scheduling mode that consists in modeling first and solving later, and most of the research focuses on the improvement of FJSP modeling methods and optimization algorithms. However, with the development of flexible job shops into intelligent manufacturing systems, manufacturing enterprises have accumulated a large amount of historical scheduling big data, and have a more accurate real-time scheduling big data acquisition ability. In this context, the research on how to make full use of the scheduling knowledge contained in the scheduling big data and on how to combine big data technology, artificial intelligence technology, and Internet of Things technology to form an autonomous, intelligent and prescient scheduling mode still needs to be further deepened.  
In this paper, a flexible job shop dynamic scheduling framework is constructed under an intelligent manufacturing environment. Combining intelligent manufacturing with dynamic shop scheduling, a dynamic scheduling model is established. To solve the problem of rush order insertion in production, a dynamic scheduling method is proposed. In the intelligent manufacturing scheduling mode, the scheduling process is realized through information fusion and interaction, and the validity of the proposed model is verified by a genetic algorithm. The innovation of this paper is that compared with the traditional workshop, in the intelligent manufacturing workshop, the production data of the actual workshop can be uploaded to the database. Through the cooperative operations of the processed data and the upper system, the dynamic scheduling of order insertion is realized, which improves production efficiency.  
The paper is organized as follows. The dynamic scheduling framework of the flexible job shop based on digital twins is constructed in Section 2. The flexible job shop dynamic scheduling problem is described in Section 3. Section 4 describes the genetic algorithm for model solving. The dynamic scheduling process of inserting orders is reported in Section 5. Example verification is described in Section 6. Finally, in Section 7, the conclusions are reported.