Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

To solve the problems of low efficiency and insufficient dynamic response of job shop scheduling in the discrete manufacturing process, a multi-objective flexible job shop scheduling model for digital twin and its solution method are proposed. Firstly, a digital twin scheduling model with physical entity, virtual model and production plan is constructed, and four factors are taken as optimization goals. Then, a hybrid particle swarm optimization method is designed to increase the refined optimization ability, and the obtained Pareto optimal solution set is analyzed by grey relational analysis to obtain a satisfactory solution which coincides with the actual production. Finally, a three-dimensional model which is completely mapped with the real job shop scheduling is built by Plant Simulation software. The scheduling process is simulated and optimized by combining with the production data of an enterprise, which verifies the feasibility and applicability of this method, and will effectively guide the production practice.

Automated summary

This paper proposes a multi-objective flexible job shop scheduling model based on digital twin and designs a hybrid particle swarm optimization method to solve the problem. The obtained Pareto optimal solution set is analyzed to obtain a satisfactory solution. A three-dimensional model is built using Plant Simulation software, and the scheduling process is simulated and optimized by combining with the production data of an enterprise to verify the feasibility and applicability of the method.