Multi-Fidelity Modeling for Analysis and Optimization of Serial Production Lines

Recent advances in sensing, data analytics and manufacturing technologies (e.g., 3D printing, soft robotics, nanotechnologies) provide the potential to produce highly customized products by allowing flexible system design, endless device configurations, and unprecedented information flows. These opportunities also increase the complexity of controlling such systems optimally, which typically requires fast exploration of an increasingly large number of alternative operation strategies. Simulation and stochastic models have been particularly successful to support control and optimization of production systems, and methods have been developed to exploit them separately. Herein, we argue that the simultaneous use of these models can allow for better control and optimization by balancing the simulation accuracy, and related high computational costs, with the computational efficiency and lower accuracy of stochastic models.