Simulation sensitivity analysis: A frequency domain approach
WSC '81 Proceedings of the 13th conference on Winter simulation - Volume 2
An experimental procedure for simulation response surface model identification
Communications of the ACM
A tutorial on simulation optimization
WSC '92 Proceedings of the 24th conference on Winter simulation
Characterizing a nonstationary M/G/1 queue using bode plots
WSC '94 Proceedings of the 26th conference on Winter simulation
Solution to the indexing problem of frequency domain simulation experiments
WSC '91 Proceedings of the 23rd conference on Winter simulation
The global simulation clock as the frequency domain experiment index
WSC '88 Proceedings of the 20th conference on Winter simulation
Future directions in response surface methodology for simulation
WSC '87 Proceedings of the 19th conference on Winter simulation
Simulation optimization methodologies
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Techniques for simulation response optimization
Operations Research Letters
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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.