Combining STRONG with screening designs for large-scale simulation optimization

IIE Transactions (Institute of Industrial Engineers)

Volumn 46, Issue 4, 2014, Pages 357-373

  Chang, Kuo Hao ^a   Li, Ming Kai ^a   Wan, Hong ^b  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b SCHOOL OF INDUSTRIAL ENGINEERING   (United States)

Author keywords

large scale problems; screening designs; simulation optimization; STRONG

Indexed keywords

COMPUTATIONAL ADVANTAGES; COMPUTATIONAL PERFORMANCE; HIGHER-DIMENSIONAL PROBLEMS; LARGE-SCALE PROBLEM; RESPONSE SURFACE METHODOLOGY; SCREENING DESIGN; SIMULATION OPTIMIZATION; STRONG;

DESIGN; DESIGN OF EXPERIMENTS; REGRESSION ANALYSIS; SURFACE PROPERTIES;

OPTIMIZATION;

EID: 84897833983     PISSN: 0740817X     EISSN: 15458830     Source Type: Journal    
DOI: 10.1080/0740817X.2013.812268     Document Type: Article

References (28)

1. Andrews, D.W.K. (1992) Generic uniform convergence. Econometric Theory, 8, 241-257.
2. Barton, R.R. and Ivey, J.S. (1996) Nelder-Mead simplex modifications for simulation optimization. Management Science, 42(7), 954-973.
3. Chang, K.-H. (2012) Stochastic Nelder-Mead simplex method v. a new globally convergent direct search method for simulation optimization. European Journal of Operational Research, 220, 684-694.
4. Chang, K.-H., Hong, L.J. and Wan, H. (2013) Stochastic trust-region response-surface method (STRONG)-a new response-surface framework for simulation optimization. INFORMS Journal on Computing, 25(2), 230-243.
5. Chang, K.-H., Li, M.-K. and Wan, H. (2011) Combining STRONG and screening designs for large-scale simulation optimization, in Jain, S., Creasey, R.R., Himmelspach, J., White, K.P. and Fu, M. (eds), Proceedings of the 2011 Winter Simulation Conference, pp. 4127-4136.
6. Conn, A.R., Gould, N.L.M. and Toint, P.L. (2000) Trust-Region Methods, SIAM, Philadelphia, PA.
7. Fu, M.C. (2002) Optimization for simulation: theory vs. practice. INFORMS Journal on Computing, 14, 192-227.
8. Fu, M.C. (2006) Gradient estimation, in Handbooks in Operations Research and Management Science. Vol. 13. Simulation, Henderson, S.G. and Nelson, B.L. (eds), Elsevier, Amsterdam, The Netherlands, pp. 575-616.
9. Kleijnen, J.P.C. (2008) Design and Analysis of Simulation Experiments, Springer, New York, NY.
10. Kushner, H.J. and Yin, G. (2010) Stochastic Approximation and Recursive Algorithms and Applications, Springer-Verlag, New York, NY
11. Lin, D.K.J. (2000) Recent developments in supersaturated designs, in Statistical Process Monitoring and Optimization, Park, S.H. and Vining, G.G. (eds), CRC Press, New York, NY, pp. 305-319.
12. Montgomery, D.C. (2009) Design and Analysis of Experiments, seventh edition, John Wiley & Sons., New York, NY.
13. More, J.J., Garbow, B.S. and Hillstrom, K.E. (1981) Testing unconstrained optimization software. ACM Transactions on Mathematical Software, 7, 17-41.
14. Morris, M.D. (2006) An overview of group factor screening, in Screening: Methods for Experimentation in Industry, Dean, A. and Lewis, S. (eds), Springer-Verlag, New York, NY, pp. 191-206.
15. Myers, R.H., Montgomery, D.C. and Anderson-Cook, C.M. (2009) Response Surface Methodology-Process and Product Optimization Using Designed Experiments, John Wiley & Sons., New York, NY.
16. Robbins, H. and Monro, S. (1951) A stochastic approximation method. The Annals of Mathematical Statistics, 22, 400-407.
17. Sakalauskas, L. and Krarup, J. (2006) Editorial; heuristic and stochastic methods in optimization. European Journal of Operational Research, 171(3), 723-724.
18. Sanchez, S.M., Wan, H. and Lucas, T.W. (2009) Two-phase screening procedures for simulation experiments. ACMTransactions on Modeling and Computer Simulation, 19(2), 1-24.
19. Shang, Y.-W. and Qiu, Y.-H. (2006) A note on the extended Rosenbrock function. Evolutionary Computation, 14(1), 119-126.
20. Shapiro, A., Dentcheva, D. and Ruszczyński, A. (2009) Lectures on Stochastic Programming: Modeling and Theory, SIAM, Philadelphia, PA.
21. Shen, H. and Wan, H. (2009) Controlled sequential factorial design for simulation factor screening. European Journal of Operational Research, 198, 511-519.
22. Shen, H., Wan, H. and Sanchez, S.M. (2010) A hybrid method for simulation factor screening. Naval Research Logistics, 57, 45-57.
23. Spall, J.C. (2003) Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, John Wiley&Sons., New York, NY.
24. Tekin, E. and Sabuncuoglu, I. (2004) Simulation optimization: a comprehensive review on theory and applications. IIE Transactions, 36, 1067-1081.
25. Trocine, L. and Malone, L.C. (2000) Finding important independent variables through screening designs: a comparison of methods, in Proceedings of the 2000 Winter Simulation Conference, Joines, J.A., Barton, R.R., Kang, K. and Fishwick, P.A. (eds), pp. 749-754.
26. Trocine, L. and Malone, L.C. (2001) An overview of newer, advanced screening methods for the initial phase in an experimental design, in Proceedings of the 2001 Winter Simulation Conference, Peters, B.A., Smith, J.S., Medeiros, D.J., Rohrer, M.W. (eds), pp. 169-178.
27. Wan, H., Ankenman, B. and Nelson, B.L. (2006) Controlled sequential bifurcation: a new factor-screening method for discrete-event simulation. Operations Research, 54, 743-755.
28. Wan, H., Ankenman, B.E. and Nelson, B.L. (2010) Improving the efficiency and efficacy of controlled sequential bifurcation for simulation factor screening. INFORMS Journal on Computing, 22(3), 482-492.