### Abstract

Original language | English |
---|---|

Title of host publication | Proceedings of the 2010 Winter Simulation Conference |

Editors | B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, E. Yucesan |

Place of Publication | Piscataway, NJ |

Publisher | IEEE |

Pages | 1283-1294 |

Publication status | Published - 2010 |

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### Cite this

*Proceedings of the 2010 Winter Simulation Conference*(pp. 1283-1294). Piscataway, NJ: IEEE.

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*Proceedings of the 2010 Winter Simulation Conference.*IEEE, Piscataway, NJ, pp. 1283-1294.

**Parametric and distribution-free bootstrapping in robust simulation-optimization.** / Dellino, G.; Kleijnen, Jack P.C.; Meloni, C.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Parametric and distribution-free bootstrapping in robust simulation-optimization

AU - Dellino, G.

AU - Kleijnen, Jack P.C.

AU - Meloni, C.

PY - 2010

Y1 - 2010

N2 - Most methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi’s world view with either regression or Kriging (also called Gaussian Process) metamodels (emulators, response surfaces, surrogates). These metamodels are combined with Non-Linear Mathematical Programming (NLMP) to find robust solutions. Varying the constraint values in this NLMP gives an estimated Pareto frontier. To account for the variability of this estimated Pareto frontier, this contribution considers different bootstrap methods to obtain confidence regions for a given solution. This methodology is illustrated through some case studies selected from the literature.

AB - Most methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi’s world view with either regression or Kriging (also called Gaussian Process) metamodels (emulators, response surfaces, surrogates). These metamodels are combined with Non-Linear Mathematical Programming (NLMP) to find robust solutions. Varying the constraint values in this NLMP gives an estimated Pareto frontier. To account for the variability of this estimated Pareto frontier, this contribution considers different bootstrap methods to obtain confidence regions for a given solution. This methodology is illustrated through some case studies selected from the literature.

M3 - Conference contribution

SP - 1283

EP - 1294

BT - Proceedings of the 2010 Winter Simulation Conference

A2 - Johansson, B.

A2 - Jain, S.

A2 - Montoya-Torres, J.

A2 - Hugan, J.

A2 - Yucesan, E.

PB - IEEE

CY - Piscataway, NJ

ER -