Developing efficient simulation methodology for complex queueing networks

Simulation can provide insight to the behavior of a complex queueing system by identifying the response surface of several performance measures such as delays and backlogs. However, simulations of large systems are expensive both in terms of CPU time and use of available resources (e.g. processors). Thus, it is of paramount importance to carefully select the inputs of simulation in order to adequately capture the underlying response surface of interest and at the same time minimize the required number of simulation runs. In this study, we present a methodological framework for designing efficient simulations for complex networks. Our approach works in sequential and combines the methods of CART (classification and regression trees) and the design of experiments. A generalized switch model is used to illustrate the proposed methodology and some useful applications are described.