Simulation Optimization: An Introductory Tutorial on Methodology

With an upward trend for use in real-world problems of high uncertainty, the field of simulation optimization (SO) is evolving to aid in finding near-optimal solutions more rapidly and reliably. A comprehensive overview of the vast and diverse literature in continuous and discrete SO over large spaces is difficult. This short tutorial intends instead to introduce the methodological landscape, stirring a middle ground between statistical analysis of Monte Carlo sampling and mathematical analysis of numerical optimization. Particular attention to sampling and its impact on SO Algorithms highlights open and promising research directions.