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

aim at the optimization of the simulated system, allowing multiple random simulation outputs. …

simulation. DOE may have two goals: sensitivity analysis including factor screening and optimization. This contribution starts … Bifurcation. Then it discusses Kriging including Latin Hyper cube Sampling and sequential designs. It ends with optimization … robust optimization. …

Whereas Operations Research has always paid much attention to optimization, practitioners judge the robustness of the … combination of the following four proven techniques: (1) discrete-event simulation, (2) heuristic optimization, (3) risk or … uncertainty analysis, and (4) bootstrapping.This methodology is illustrated through a case study on production control systems …

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 …

Sensitivity analysis may serve validation, optimization, and risk analysis of simulation models.This review surveys …