Simulation Optimization for a Digital Twin Using a Multi-Fidelity Framework

Abstract

Digital twin technology is increasingly ubiquitous in manufacturing and there is a need to increase the efficiency of optimization methods that use digital twins to answer questions about the real system. These methods typically support short-term operational decisions and, as a result, optimization methods need to return results in real or near-to-real time. This is especially challenging in manufacturing systems as the simulation models are typically large and complex. In this article, we describe an algorithm for a multi-fidelity model that uses a simpler low-fidelity neural network metamodel in the first stage of the optimization and a high-fidelity simulation model in the second stage. Initial experimentation suggests that it performs well.