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Simulation optimization is often applied to solve large-scale complex systems problems in which high-fidelity simulation models are usually complex and time-consuming to run. As a result, the number of objective function evaluations is often very limited and presents a major hurdle for optimization. Recently, a new multi-fidelity optimization framework referred to as ordinal transformation was proposed...
Simulation models of different levels of fidelity are often available for evaluating alternative solutions of a complex system. High-fidelity simulations generate accurate predictions but can be very time-consuming to run. Therefore, they can only be applied to a small number of solutions. Low-fidelity simulations are much faster and can evaluate a large number of solutions. But simulation results...
Simulation can be a very powerful tool to help decision making in many applications such as semiconductor manufacturing or healthcare, but exploring multiple courses of actions can be time consuming. We propose an optimal computing budget allocation (OCBA) method to improve the efficiency of simulation optimization using parametric regression. The approach proposed here, called OCBA-DOE, incorporates...
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