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 …
Knowledge gradient is a design principle for developing Bayesian sequential sampling policies to consider in this paper the ranking and selection problem in the presence of covariates, where the best alternative is not universal but depends on the …
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, …
It is known that incorporating gradient information can significantly enhance the prediction accuracy of stochastic kriging. However, such an enhancement cannot be scaled trivially to high-dimensional design space, since one needs to invert a large …
Stochastic kriging is a popular metamodeling technique to approximate computationally expensive simulation models. However, it typically treats the simulation model as a black box in practice and often fails to capture the highly nonlinear response …
Stochastic kriging is a popular technique for simulation metamodeling due to its flexibility and analytical tractability. Its computational bottleneck is the inversion of a covariance matrix, which takes $O(n^3)$ time in general and becomes …
Stochastic kriging is a popular metamodeling technique for representing the unknown response surface of a simulation model. However, the simulation model may be inadequate in the sense that there may be a non-negligible discrepancy between it and the …
A simulation model is often used as a proxy for the real system of interest in a decision-making process. However, no simulation model is totally representative of the reality. The impact of the model inadequacy on the prediction of system …