Customized sequential designs for random simulation experiments: Kriging metamodeling and bootstrapping
This paper proposes a novel method to select an experimental design for interpolation in random simulation, especially discrete event simulation.(Though the paper focuses on Kriging, this design approach may also apply to other types of metamodels such as linear regression models.)Assuming that simulation requires much computer time, it is important to select a design with a small number of observations (or simulation runs).The proposed method is therefore sequential.Its novelty is that it accounts for the specific input/output behavior (or response function) of the particular simulation at hand; i.e., the method is customized or application-driven.A tool for this customization is bootstrapping, which enables the estimation of the variances of predictions for inputs not yet simulated.The new method is tested through two classic simulation models: example 1 estimates the expected steady-state waiting time of the M/M/1 queueing model; example 2 estimates the mean costs of a terminating (s, S) inventory simulation.For these simulations the novel design indeed gives better results than Latin Hypercube Sampling (LHS) with a prefixed sample of the same size.
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- Sridhar Bashyam & Michael C. Fu, 1998. "Optimization of (s, S) Inventory Systems with Random Lead Times and a Service Level Constraint," Management Science, INFORMS, vol. 44(12-Part-2), pages 243-256, December.
- Ferri, M. & Piccioni, M., 1992. "Optimal selection of statistical units : An approach via simulated annealing," Computational Statistics & Data Analysis, Elsevier, vol. 13(1), pages 47-61, January.
- Kleijnen, Jack P.C. & Deflandre, David, 2006. "Validation of regression metamodels in simulation: Bootstrap approach," European Journal of Operational Research, Elsevier, vol. 170(1), pages 120-131, April.
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