Monte Carlo optimization, simulation, and sensitivity of queueing networks
Monte Carlo optimization, simulation, and sensitivity of queueing networks
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Stochastic Kriging for Simulation Metamodeling
Operations Research
Better simulation metamodeling: the why, what, and how of stochastic kriging
Winter Simulation Conference
Simulation optimization using metamodels
Winter Simulation Conference
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Traditional metamodel-based optimization methods assume experiment data collected consist of performance measurements only. However, in many settings found in stochastic simulation, direct gradient estimates are available. We investigate techniques that augment existing regression and stochastic kriging models to incorporate additional gradient information. The augmented models are shown to be compelling compared to existing models, in the sense of improved accuracy or reducing simulation cost. Numerical results also indicate that the augmented models can capture trends that standard models miss.