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Convex and monotonic bootstrapped kriging
Kleijnen,Jack P.C. Mehdad,E. van Beers,W.C.M.
Kleijnen,Jack P.C.
Mehdad,E.
van Beers,W.C.M.
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.
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Date
2012
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Unknown Publisher
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Kleijnen, J P C, Mehdad, E & van Beers, W C M 2012, Convex and monotonic bootstrapped kriging. in C Laroque, J Himmelspach, R Pasupathy, O Rose & A M Uhrmacher (eds), Proceedings of the 2012 Winter Simulation Conference. Unknown Publisher, Berlin, pp. 543-554.
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info:eu-repo/semantics/restrictedAccess