Abstract: This article surveys optimization of simulated systems. The simulation may be either deterministic or random …. The survey reflects the author’s extensive experience with simulation-optimization through Kriging (or Gaussian process …-free bootstrapping (or resampling) for random simulation. The survey covers: (1) Simulation-optimization through "efficient global …

discusses robust optimization of the decision variables, while accounting for uncertainties in the environmental variables. …

optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the … improvement" (EI) in "efficient global optimization" (EGO) through the introduction of an unbiased estimator of the Kriging …

yet simulated. These predictions and their variances are used by efficient global optimization"(EGO), to balance local and …

We present a definition of increasing uncertainty, independent of any notion of subjective probabilities, or of any … particular model of preferences.Our notion of an elementary increase in the uncertainty of any act corresponds to the addition of … dual definition of comparative aversion to uncertainty.We characterize this definition for a popular class of generalized …

This paper generalizes the theory of irreversible investment under uncertainty by allowing for risk averse investors in … aversion to examine the e ects of risk aversion, price uncertainty, and other parameters on the optimal investment decision … price uncertainty increases the value of deferring irreversible investments.This e ect is stronger for high levels of risk …

practical production management situation, as it combines the standard techniques of simulation, heuristic optimization, risk …