Simulation optimization using metamodels

Many iterative optimization methods are designed to be used in conjunction with deterministic objective functions. These optimization methods can be difficult to apply to an objective generated by a discrete-event simulation, due to the stochastic nature of the response(s) and the potentially extensive run times. A metamodel aids simulation optimization by providing a deterministic objective with run times that are generally much shorter than the original discrete-event simulation. Polynomial metamodels generally provide only local approximations, and so a series of metamodels must be fit as the optimization progresses. Other classes of metamodels can provide global fit; fitting can be done either by constructing the global model once at the start of the optimization, or by using the optimization results to identify additional discrete-event runs to refine the global model. This tutorial surveys both local and global metamodel-based optimization methods.