Convex and monotonic bootstrapped kriging

Jack P.C. Kleijnen, E. Mehdad, W.C.M. van Beers

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)
323 Downloads (Pure)

Abstract

Distribution-free bootstrapping of the replicated responses of a given discrete-event simulation model gives bootstrapped Kriging (Gaussian process) metamodels; we require these metamodels to be either convex or monotonic. To illustrate monotonic Kriging, we use an M/M/1 queueing simulation with as output either the mean or the 90% quantile of the transient-state waiting times, and as input the traffic rate. In this example, monotonic bootstrapped Kriging enables better sensitivity analysis than classic Kriging; i.e., bootstrapping gives lower MSE and confidence intervals with higher coverage and the same length. To illustrate convex Kriging, we start with simulation-optimization of an (s, S) inventory model, but we next switch to a Monte Carlo experiment with a second-order polynomial inspired by this inventory simulation. We could not find truly convex Kriging metamodels, either classic or bootstrapped; nevertheless, our bootstrapped “nearly convex” Kriging does give a confidence interval for the optimal input combination.
Original languageEnglish
Title of host publicationProceedings of the 2012 Winter Simulation Conference
EditorsC. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, A.M. Uhrmacher
Place of PublicationBerlin
PublisherUnknown Publisher
Pages543-554
Publication statusPublished - 2012

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