Efficient simulation budget allocation for selecting the best set of simplest good enough designs

Proceedings - Winter Simulation Conference

Volumn , Issue , 2010, Pages 1152-1159

  Yan, Shen ^a   Zhou, Enlu ^a   Chen, Chun Hung ^b  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b George Mason University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BUDGET ALLOCATION; COMPLEX DESIGNS; EFFICIENT SIMULATION; MEMORY RESOURCES; NUMERICAL RESULTS; TEST PROBLEM;

BUDGET CONTROL;

DESIGN;

EID: 79951652689     PISSN: 08917736     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WSC.2010.5679075     Document Type: Conference Paper

References (12)

1. Andradóttir, S., D. Goldsman, B. W. Schmeiser, L. W. Schruben, and E. Yücesan. 2005. Analysis methodology: Are we done? In Proceedings of the 37th conference on Winter simulation, ed. M. E. Kuhl, N. Steiger, F. Armstrong, and J. Joines: IEEE.
2. Chen, C. H., D. H. He, M. Fu, and L. H. Lee. 2008. Efficient simulation budget allocation for selecting an optimal subset. INFORMS Journal on Computing 20 (4): 579-595.
3. Chen, C. H., J. Lin, E. Yücesan, and S. E. Chick. 2000. Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Journal of Discrete Event Dynamic Systems: Theory and Applications 10:251-270.
4. Chen, C. H., and E. Yücesan. 2005. An alternative simulation budget allocation scheme for efficient simulation. International Journal of Simulation and Process Modeling 1 (1/2): 49-57.
5. Dudewicz, E. J., and S. R. Dalal. 1975. Allocation of observation in ranking and selection with unequal variances. Sankhya: The Indian Journal of Statistics 37:28-78.
6. Jia, Q. S. 2009. An adaptive sampling algorithm for simulation-based optimization with descriptive complexity preference. under review.
7. Kar, K., A. Krishnamurthy, and N. Jaggi. 2006. Dynamic node activation in networks of rechargeable sensors. IEEE/ACM Transactions on Networking 14 (1): 15-26. (Pubitemid 43264666)
8. Koenig, L. W., and A. M. Law. 1985. A procedure for selecting a subset of size m containing the 1 best of k independent normal populations. Communications in Statistics - SImulation and Computation 14:719-734.
9. Lee, H. L., N. A. Pujowidianto, L. W. Li, C. H. Chen, and C. M. Yap. 2009. Asymptotic simulation budget allocation for selecting the best design in the present of stochastic constraints. working paper.
10. Levin, L. A. 1973. Universal sequential search problems. Problems of Information Transmission 9 (3): 265-266.
11. Szechtman, R., and E. Yücesan. 2008. A new perspective on feasibility determination. In Proceedings of the 2008 Winter Simulation Conference, ed. S. Mason, R. Hill, O. R. L. Monch, T. Jefferson, and J. Fowler, 273-280: IEEE.
12. Yan, S., E. Zhou, and C. H. Chen. 2010. Efficient simulation budget allocation for selecting an optimal good enough subset with descriptive complexity preference. working paper.