Journal
IIE TRANSACTIONS
Volume 42, Issue 1, Pages 60-70Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/07408170903116360
Keywords
Discrete-event simulation; simulation optimization; simulation uncertainty
Funding
- Department of Energy [DE-SC0002223]
- National Science Council of the Republic of China [NSC 95-2811-E-002-009]
- NSF [IIS-0325074]
- NASA Ames Research Center [NAG-2-1643, NNA05CV26G]
- FAA [00-G-016]
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Simulation plays a vital role in analyzing discrete-event systems, particularly in comparing alternative system designs with a view to optimizing system performance. Using simulation to analyze complex systems, however, can be both prohibitively expensive and time-consuming. Effective algorithms to allocate intelligently a computing budget for discrete-event simulation experiments are presented in this paper. These algorithms dynamically determine the simulation lengths for all simulation experiments and thus significantly improve simulation efficiency under the constraint of a given computing budget. Numerical illustrations are provided and the algorithms are compared with traditional two-stage ranking-and-selection procedures through numerical experiments. Although the proposed approach is based on heuristics, the numerical results indicate that it is much more efficient than the compared procedures.
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