Dynamic Production Scheduling of Digital Twin Job-Shop Based on Edge Computing

Abstract

The current production scheduling models cannot effectively enable the real-time interaction between information space and physical space. To dynamically schedule twin digital job-shop, this paper attempts to realize the dynamic scheduling of digital twin job-shop (DTJ) based on edge computing. First, the architecture of the DTJ was established by adding the digital twin between the business management layer and the operation execution layer of the traditional job-shop. On this basis, the DTJ was fully modelled, and the manufacturing process was monitored, analyzed and managed remoted by edge computing. To realize dynamic scheduling, a DTJ scheduling model was established through data mining. The model consists of two parts: a data collection model and a multi-scheduling knowledge model. Finally, the proposed DTJ scheduling model was verified through simulation on an actual job-shop. The research results shed new light on the optimization of manufacturing process in various types of job-shops.