### Abstract

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

Place of Publication | Tilburg |

Publisher | Operations research |

Number of pages | 61 |

Volume | 1997-52 |

Publication status | Published - 1997 |

### Publication series

Name | CentER Discussion Paper |
---|---|

Volume | 1997-52 |

### Fingerprint

### Keywords

- least squares
- distribution-free
- non-parametric
- stopping rule
- run-length
- Von Neumann
- median
- seed
- likelihood ratio

### Cite this

*Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models*. (CentER Discussion Paper; Vol. 1997-52). Tilburg: Operations research.

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**Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models.** / Kleijnen, J.P.C.

Research output: Working paper › Discussion paper › Other research output

TY - UNPB

T1 - Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models

AU - Kleijnen, J.P.C.

N1 - Pagination: 61

PY - 1997

Y1 - 1997

N2 - This chapter gives a survey on the use of statistical designs for what-if analysis in simula- tion, including sensitivity analysis, optimization, and validation/verification. Sensitivity analysis is divided into two phases. The first phase is a pilot stage, which consists of screening or searching for the important factors among (say) hundreds of potentially important factors. A novel screening technique is presented, namely sequential bifurcation. The second phase uses regression analysis to approximate the input/output transformation that is implied by the simulation model; the resulting regression model is also known as a metamodel or a response surface. Regression analysis gives better results when the simu- lation experiment is well designed, using either classical statistical designs (such as frac- tional factorials) or optimal designs (such as pioneered by Fedorov, Kiefer, and Wolfo- witz). To optimize the simulated system, the analysts may apply Response Surface Metho- dology (RSM); RSM combines regression analysis, statistical designs, and steepest-ascent hill-climbing. To validate a simulation model, again regression analysis and statistical designs may be applied. Several numerical examples and case-studies illustrate how statisti- cal techniques can reduce the ad hoc character of simulation; that is, these statistical techniques can make simulation studies give more general results, in less time. Appendix 1 summarizes confidence intervals for expected values, proportions, and quantiles, in termi- nating and steady-state simulations. Appendix 2 gives details on four variance reduction techniques, namely common pseudorandom numbers, antithetic numbers, control variates or regression sampling, and importance sampling. Appendix 3 describes jackknifing, which may give robust confidence intervals.

AB - This chapter gives a survey on the use of statistical designs for what-if analysis in simula- tion, including sensitivity analysis, optimization, and validation/verification. Sensitivity analysis is divided into two phases. The first phase is a pilot stage, which consists of screening or searching for the important factors among (say) hundreds of potentially important factors. A novel screening technique is presented, namely sequential bifurcation. The second phase uses regression analysis to approximate the input/output transformation that is implied by the simulation model; the resulting regression model is also known as a metamodel or a response surface. Regression analysis gives better results when the simu- lation experiment is well designed, using either classical statistical designs (such as frac- tional factorials) or optimal designs (such as pioneered by Fedorov, Kiefer, and Wolfo- witz). To optimize the simulated system, the analysts may apply Response Surface Metho- dology (RSM); RSM combines regression analysis, statistical designs, and steepest-ascent hill-climbing. To validate a simulation model, again regression analysis and statistical designs may be applied. Several numerical examples and case-studies illustrate how statisti- cal techniques can reduce the ad hoc character of simulation; that is, these statistical techniques can make simulation studies give more general results, in less time. Appendix 1 summarizes confidence intervals for expected values, proportions, and quantiles, in termi- nating and steady-state simulations. Appendix 2 gives details on four variance reduction techniques, namely common pseudorandom numbers, antithetic numbers, control variates or regression sampling, and importance sampling. Appendix 3 describes jackknifing, which may give robust confidence intervals.

KW - least squares

KW - distribution-free

KW - non-parametric

KW - stopping rule

KW - run-length

KW - Von Neumann

KW - median

KW - seed

KW - likelihood ratio

M3 - Discussion paper

VL - 1997-52

T3 - CentER Discussion Paper

BT - Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models

PB - Operations research

CY - Tilburg

ER -