Supercharging Digital Twins with AI

The increasing availability of large data sets has enabled the development of digital twins (DTs), virtual models that accurately replicate real-world systems. This paper explores how AI and machine learning (AI/ML) can enhance the efficiency of stochastic simulations, particularly optimization via simulation, to strengthen the predictive capabilities of DTs and drive better decision-making. To achieve this, we present a structured framework for simulation optimization that formally establishes the role of AI/ML in improving decision-making within DTs. We introduce innovative methodologies, SAMPLE and TRAIN, designed to enhance the efficiency of simulation optimization by integrating AI with simulation tools. These approaches transform complex problems into AI models, reducing the need for extensive observations while enabling efficient solutions for intricate manufacturing systems. They also improve constraint handling by converting chance constraints into conditional value-at-risk (CVaR) constraints and estimating them efficiently through CVaR regression. By leveraging information from both constraints and objective functions, these frameworks facilitate the effective application of general optimization techniques, accelerating the search process within the metamodel. Moreover, they ensure the accuracy of metamodels, strengthening their reliability in decision support. We conclude with insights into future opportunities for leveraging AI/ML techniques to further enhance and expand the capabilities of digital twins, unlocking new pathways for operational efficiency and strategic value creation.