Simulation-optimization via Kriging and bootstrapping: a survey

Jack Kleijnen ()

Journal of Simulation, 2014, vol. 8, issue 4, 241-250

Abstract: This article surveys optimization of simulated systems. The simulation may be either deterministic or random. The survey reflects the author’s extensive experience with simulation-optimization through Kriging (or Gaussian process) metamodels, analysed through parametric bootstrapping for deterministic and random simulation and distribution-free bootstrapping (or resampling) for random simulation. The survey covers: (1) simulation-optimization through ‘efficient global optimization’ using ‘expected improvement’ (EI); this EI uses the Kriging predictor variance, which can be estimated through bootstrapping accounting for the estimation of the Kriging parameters; (2) optimization with constraints for multiple random simulation outputs and deterministic inputs through mathematical programming applied to Kriging metamodels validated through bootstrapping; (3) Taguchian robust optimization for uncertain environments, using mathematical programming—applied to Kriging metamodels—and bootstrapping to estimate the variability of the Kriging metamodels and the resulting robust solution; (4) bootstrapping for improving convexity or preserving monotonicity of the Kriging metamodel.

Date: 2014
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DOI: 10.1057/jos.2014.4

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