Convex and monotonic bootstrapped kriging
| Autor: | Kleijnen, Jack P.C., Mehdad, E., van Beers, W.C.M., Laroque, C., Himmelspach, J., Pasupathy, R., Rose, O., Uhrmacher, A.M. |
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| Přispěvatelé: | Research Group: Operations Research |
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: | |
| Zdroj: | Proceedings of the 2012 Winter Simulation Conference, 543-554 STARTPAGE=543;ENDPAGE=554;TITLE=Proceedings of the 2012 Winter Simulation Conference |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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