基于上述动机，本文将数字孪生技术引入多目标的柔性作业车间调度中，提出了

Based on the above motivation, this paper introduces the digital twin technology into the multi-objective flexible job shop scheduling, and proposes a digital twin-oriented multi-objective flexible job shop scheduling model and its solution method. First, build a digital twin scheduling model and determine the optimization goal. Then, a hybrid particle swarm optimization algorithm is designed to obtain the pareto optimal solution set of the scheduling problem, and the gray relational analysis is used to obtain a satisfactory solution. Finally, the plant simulation software is used to establish a virtual scheduling workshop, and the production example data is used for scheduling simulation to verify the feasibility of the method.

Based on the above motivation, this paper introduces digital twinning technology into multi-objective flexible job shop scheduling, and proposes a multi-objective flexible job shop scheduling model for digital twinning and its solution method. Firstly, the digital twin scheduling model is constructed and the optimization goal is determined. Then, a hybrid particle swarm optimization algorithm is designed to obtain the Pareto optimal solution set of the scheduling problem, and the satisfactory solution is obtained by grey correlation analysis. Finally, the virtual scheduling workshop is established by using plant simulation software, and the scheduling simulation is carried out by using production example data to verify the feasibility of this method.<br>

Based on the above motivation, this paper introduces digital twinning technology into multi-objective flexible job shop scheduling, and proposes a multi-objective flexible job shop scheduling model and its solution method for digital twinning. Firstly, the digital twin scheduling model is constructed and the optimization objective is determined. Then, a hybrid particle swarm optimization algorithm is designed to obtain the pareto optimal solution set of the scheduling problem, and a satisfactory solution is obtained by grey relational analysis. Finally, the virtual scheduling workshop is established by using the plant simulation software, and the scheduling simulation is carried out by using the production example data to verify the feasibility of this method.