simulation optimization using frequency domain methods
Simulation programs can be quite complex, sometimes involving a great number of input factors and parameters. Conventional experiments where each setting of the input values requires a separate simulation run can be quite time consuming and expensive; such experiments will be referred to as run oriented simulation experiments. An alternative approach is to allow the input variables in a simulation to vary according to specific patterns during a single run. Various output spectra can then be analyzed to gain information about the simulation; such experiments will be referred to as frequency-domain simulation experiments. This technique was initially developed primarily to aid in factor screening and to perform a global sensitivity analysis of the input parameters in a simulation. It has since been developed into a method for identifying a meta-model for the simulation response surface. In this paper an overview of frequency domain simulation experiments is first presented. A new technique for including discrete factors in frequency domain experiments will also be discussed. Recently frequency domain optimality criteria have been discovered that can be used to identify local optima in the simulation response. The focus of this paper is on these optimality criteria. A brief discussion of how optimization algorithms might be designed using frequency domain simulation experiments is presented. This last topic is the subject of current research in frequency domain simulation experiments.
simulation optimality method program complexity input factor experience values timing variability pattern informal global sensitivity analysis meta-model response surface paper locality algorithm research