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Optimal budget allocation for discrete-event simulation experiments

2009; Taylor & Francis; Volume: 42; Issue: 1 Linguagem: Inglês

10.1080/07408170903116360

1545-8830

Chun‐Hung Chen, Enver Yücesan, Liyi Dai, Hsiao-Chang Chen,

Modeling, Simulation, and Optimization

Abstract 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. Keywords: Discrete-event simulationsimulation optimizationsimulation uncertainty Acknowledgements This work has been supported in part by the Department of Energy under grant DE-SC0002223 the National Science Council of the Republic of China under grant NSC 95-2811-E-002-009, by NSF under grant IIS-0325074, by NASA Ames Research Center under grants NAG-2-1643 and NNA05CV26G and by the FAA under grant 00-G-016. The authors would like to thank Professors David Goldsman, Stephen Chick and Barry Nelson for their helpful suggestions and valuable comments, which significantly improved the exposition of this paper.