Dynamic Data Scheduling of a Flexible Industrial Job Shop Based on Digital Twin Technology

Abstract

Aiming at the problems of premature convergence of existing workshop dynamic data scheduling methods and the decline in product output, a flexible industrial job shop dynamic data scheduling method based on digital twin technology is proposed. First, digital twin technology is proposed, which provides a design and theoretical basis for the simulation tour of a flexible industrial job shop, building the all-factor digital information fusion model of a flexible industrial workshop to comprehensively control the all-factor digital information of the workshops. A CGA algorithm is proposed by introducing the cloud model. The algorithm is used to solve the model, and the chaotic particle swarm optimization algorithm is used to maintain the particle diversity to complete the dynamic data scheduling of a flexible industrial job shop. The experimental results show that the designed method can complete the coordinated scheduling among multiple production lines in the least amount of time.

Knowledge-data Collaborated Digital Twin Model of Papermaking Process

“…The papermaking process has the following characteristics: diverse raw materials with different nature and complex interrelationships in the process, and there are a large number of parameters that are difficult to be detected along the process; various complicated physical and chemical reactions occur in its process [30].…”