Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of complex simulation models. However, its use is limited to cases where the design space is low-dimensional because, in general, the sample …
Simulation models are widely used in practice to facilitate decision making in a complex, dynamic and stochastic environment. But they are computationally expensive to execute and optimize because of a lack of analytical tractability. Simulation …
High-dimensional simulation optimization is notoriously challenging. We propose a new sampling algorithm that converges to a global optimal solution and suffers minimally from the curse of dimensionality. The algorithm consists of two stages. First, …