Learning Simulation-Based Digital Twins for Discrete Material Flow Systems: A Review

Digital Twins (DTs) play a crucial role in the fourth industrial revolution. In the context of discrete material flow systems, companies under constant competitive pressure seek solutions to minimize costs and maximize performance. Simulation-based DTs can help make optimal decisions in the design, planning, and control of these systems. Such DTs are until now created and updated by domain experts producing costs and are often not considering the advances made in machine learning to improve prediction quality. Learning DTs out of data could be the solution for a broader application. A lot of work has already been done that contributes to this endeavor, yet relevant building blocks originate from different scientific areas resulting in the use of different terminology. Thus we present a holistic review of relevant work and analyze the state of the art based on a new classification scheme deriving relevant building blocks and gaps for future research.