An efficient simulation budget allocation method incorporating regression for partitioned domains

Automatica

Volumn 50, Issue 5, 2014, Pages 1391-1400

  Brantley, Mark W ^a   Lee, Loo Hay ^b,c   Chen, Chun Hung ^d   Xu, Jie ^d  

^a Goose Point Analysis LLC   (United States)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c SHANGHAI MARITIME UNIVERSITY   (China)

^d Krasnow Institute for Advanced Study   (United States)

Author keywords

Budget allocation; Regression; Simulation

Indexed keywords

DESIGN; EFFICIENCY; NUMERICAL METHODS; REGRESSION ANALYSIS;

BUDGET ALLOCATION; EFFICIENT SIMULATION; NUMERICAL EXPERIMENTS; RANKING AND SELECTION; REGRESSION; REGRESSION METAMODELS; SIMULATION; SIMULATION EFFICIENCY;

BUDGET CONTROL;

EID: 84899981424     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2014.03.011     Document Type: Article

References (23)

1. A.C. Atkinson, and A.N. Donev Optimum experimental designs 1998 Oxford Science Publications Oxford
2. R.E. Bechhofer, T.J. Santner, and D.M. Goldsman Design and analysis of experiments for statistical selection, screening, and multiple comparisons 1995 John Wiley & Sons New York
3. J. Branke, S. Chick, and C. Schmidt Selecting a selection procedure Management Science 53 2007 1916 1932
4. Brantley, M. W. (2011). Simulation-based stochastic optimization on discrete domains: integrating optimal computing and response surfaces. Ph.D. thesis. George Mason University.
5. M.W. Brantley, L.-H. Lee, and C.-H. Chen Efficient simulation budget allocation with regression IIE Transactions 45 2013 291 308
6. F. Burden, and J. Faires Numerical analysis 3rd ed. 1993 PWS Publishers Boston, Massachusetts
7. K. Chang, L. Hong, and H. Wan Stochastic trust-region response-surface method (strong) - a new response-surface framework for simulation optimization INFORMS Journal on Computing 25 2013 230 243
8. C.-H. Chen, D. He, M. Fu, and L.-H. Lee Efficient simulation budget allocation for selecting an optimal subset INFORMS Journal on Computing 20 2008 579 595
9. C.-H. Chen, and L.-H. Lee Stochastic simulation optimization: an optimal computing budget allocation 2011 World Scientific Publishing Co. Singapore
10. C.-H. Chen, J. Lin, E. Yücesan, and S.E. Chick Simulation budget allocation for further enhancing the efficiency of ordinal optimization Journal of Discrete Event Dynamic Systems: Theory and Applications 10 2000 251 270
11. C.-H. Chen, E. Yücesan, L. Dai, and H.-C. Chen Efficient computation of optimal budget allocation for discrete event simulation experiment IIE Transactions 42 2010 60 70
12. S.E. Chick, and K. Inoue New two-stage and sequential procedures for selecting the best simulated system Operations Research 49 2001 1609 1624
13. M.H. DeGroot Optimal statistical decisions 1970 McGraw-Hill New York
14. A. de la Garza Spacing of information in polynomial regression The Annals of Mathematical Statistics 25 1954 123 130
15. J.H. Friedman Multivariate adaptive regression splines The Annals of Statistics 19 1991 1 67
16. Glynn, P. W.; Juneja, S. (2004). A large deviations perspective on ordinal optimization. In Proceedings of the 2004 winter simulation conference (pp. 577-585).
17. L.-H. Lee, C.-H. Chen, E.-P. Chew, J. Li, N.A. Pujowidianto, and S. Zhang A review of optimal computing budget allocation algorithms for simulation optimization problem International Journal of Operations Research 72 2010 19 31
18. L.-H. Lee, A. Pujowidianto, L.-W. Li, C.-H. Chen, and C.-M. Yap Approximate simulation budget allocation for selecting the best design in the presence of stochastic constraints IEEE Transactions on Automatic Control 57 2012 2940 2945
19. Morrice, D. J.; Brantley, M. W.; Chen, C.-H. (2008). An efficient ranking and selection procedure for a linear transient mean performance measure. In Proceedings of the 2008 winter simulation conference (pp. 290-296).
20. Y. Rinott On two-stage selection procedures and related probability inequalities Communications in Statistics A7 1978 799 811
21. J.C. Spall Introduction to stochastic search and optimization: estimation, simulation, and control 2003 John Wiley & Sons Hoboken, New Jersey
22. A. Törn, and A. Žilinskas Global optimization G. Goos, J. Hartmanis, Lecture notes in computer science Vol. 350 1989 Springer-Verlag Berlin
23. M. Yang On the de la Garza phenomenon Annals of Statistics 38 2010 2499 2524