# Dynamic Scheduling Optimization of Production Workshops Based on Digital Twin
## 1 Introduction  
In recent years, with the rapid development of information technology and artificial intelligence technology, new requirements and directions have been put forward for the traditional manufacturing industry, and the transformation from traditional manufacturing to intelligent manufacturing will be the development trend of all manufacturing enterprises. The discrete production mode widely exists in aerospace enterprises, and its workshop scheduling problem under the influence of dynamic disturbance factors urgently needs to be solved. This has symbolic significance for the realization of intelligent flexible production. From time to time, there are uncertain and unexpected events in workshop operation, such as equipment failure [1], which lead to deviation of the actual production schedule from the plan. Dynamic scheduling is a way to adjust the original plan after uncertain emergencies, which is more in line with the requirements of complex manufacturing environment in actual production. Rescheduling is the main method to realize dynamic job-shop scheduling. The two most critical issues of the rescheduling method are the trigger mechanism of the rescheduling method and the rescheduling method, i.e., \"when\" and \"how\" to re-schedule. There are three typical methods to study \"when\" rescheduling, which is event-driven rescheduling, periodic rescheduling and event-cycle based hybrid rescheduling. [PERSON] et al. [2] proposed a complete rescheduling method for multiple parallel machines based on the event-driven strategy. [PERSON] et al. [3] studied the dynamic scheduling method based on the rolling time-domain optimization strategy based on the cycle drive. [PERSON] et al. [4] and [PERSON] et al. [5] studied the dynamic scheduling problemof workshop by using the method based on the hybrid drive of cycle and time. Among the three trigger mechanisms, event-driven is more suitable for the situation where an emergency affects normal production and is suitable for dynamic scheduling in the complex environment of a workshop. As for the \"how to\" rescheduling, in order to achieve high response and fast feedback of dynamic scheduling, [PERSON] et al. [6] introduced three rescheduling strategies, namely reorganization scheduling, crossover scheduling and insertion scheduling, to handle dynamic events such as new job insertion, old job cancellation, and machine failure. [PERSON] et al. [7] addressed the problem of flexible job workshop scheduling process cannot dynamically adjust key parameters, a self-learning genetic algorithm is proposed, which uses genetic algorithm as the basic optimization method and intelligently adjusts its key parameters based on reinforcement learning. For flexible Job-shop scheduling under machine faults, [PERSON] et al. [8] proposed a two-stage Particle Swarm Optimization. [PERSON] et al. [9] studied the dynamic scheduling problem under events such as random arrival of tasks, machine failure and processing time change, and proposed a hybrid genetic algorithm combining taboo search and genetic algorithm. [PERSON] et al. [10], aiming at minimizing the advance, delay and delivery time of operations, used genetic algorithm and the ant colony algorithm to solve the dynamic scheduling problem of job shop with random arrival. [PERSON] et al. [11] used fruit fly algorithm to optimize completion time, energy consumption and equipment utilization rate, realizing dynamic scheduling of workshop and ensuring timely response of scheduling. However, the application of dynamic scheduling has always been a problem in practical production. Due to the complex dynamic and changeable production environment, it is impossible to obtain the production status in time to make a reasonable scheduling plan. In dynamic scheduling under the influence of disturbance, it is relatively difficult to realize the fast feedback of dynamic scheduling when a disturbance has occurred.