Stochastic optimization is vital to making sound
engineering and business decisions under uncertainty.
While the limited capability of handling complex
domain structures and random variables renders
analytic methods helpless in many circumstances,
stochastic optimization based on simulation is widely
applicable. This work extends the traditional
response surface methodology into a surrogate model
framework to address high dimensional stochastic
problems. The framework integrates Latin hypercube
sampling (LHS), domain reduction techniques, least
square support vector machine (LSSVM) and...