Efficient experimental design tools for exploring large simulation models

Simulation experiments are typically faster, cheaper and more flexible than physical experiments. They are especially useful for pilot studies of complicated systems where little prior knowledge of the system behavior exists. One key characteristic of simulation experiments is the large number of factors and interactions between factors that impact decision makers. Traditional simulation approaches offer little help in analyzing large numbers of factors and interactions, which makes interpretation and application of results very difficult and often incorrect. In this paper we implement and demonstrate efficient design of experiments techniques to analyze large, complex simulation models. Looking specifically within the domain of organizational performance, we illustrate how our approach can be used to analyze even immense results spaces, driven by myriad factors with sometimes unknown interactions, and pursue optimal settings for different performance measures. This allows analysts to rapidly identify the most important, results-influencing factors within simulation models, employ an experimental design to fully explore the simulation space efficiently, and enhance system design through simulation. This dramatically increases the breadth and depth of insights available through analysis of simulation data, reduces the time required to analyze simulation-driven studies, and extends the state of the art in computational and mathematical organization theory.