## Abstract

Suppose that there are k ≥ 2 different systems (i.e., stochastic processes), where each system has an unknown steady-state mean performance. We consider the problem of running a two-stage simulation using common random numbers to construct fixed-width confidence intervals for two multiple-comparison problems. Under the assumptions that the stochastic processes representing the simulation output of the different systems satisfy a functional central limit theorem and that the asymptotic covariance matrix satisfies a condition known as sphericity, we prove that our confidence intervals are asymptotically valid (as the desired half-width of the confidence intervals tend to zero). We develop both absolute- and relative-width confidence intervals. Empirical results are presented indicating the procedures' robustness to violations of the sphericity assumption.

Original language | English (US) |
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Pages (from-to) | 1330-1349 |

Number of pages | 20 |

Journal | European Journal of Operational Research |

Volume | 182 |

Issue number | 3 |

DOIs | |

State | Published - Nov 1 2007 |

## All Science Journal Classification (ASJC) codes

- General Computer Science
- Modeling and Simulation
- Management Science and Operations Research
- Information Systems and Management

## Keywords

- Common random numbers
- Functional central limit theorem
- Multiple comparisons
- Output analysis
- Simulation