A brief introduction to optimization via simulation

Proceedings - Winter Simulation Conference

Volumn , Issue , 2009, Pages 75-85

  Hong, L Jeff ^a   Nelson, Barry L ^b  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b Northwestern University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

RESEARCH DEVELOPMENT;

EID: 77951527013     PISSN: 08917736     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WSC.2009.5429321     Document Type: Conference Paper

References (49)

1. Alrefaei, M. H., and S. Andradóttir. 1999. A simulated annealing algorithm with constant temperature for discrete stochastic optimization. Management Science 45:748-764.
2. Andradóttir, S. 1995. A method for discrete stochastic optimization. Management Science 41:1946-1961.
3. Andradóttir, S. 1999. Accelerating the convergence of random search methods for discrete stochastic optimization. ACM Transactions on Modeling and Computer Simulation 9:349-380.
4. Andradóttir, S. 2006. Simulation optimization with countably infinite feasible regions: Efficiency and convergence. ACM Transactions on Modeling and Computer Simulation 16:357-374. (Pubitemid 44664468)
5. Banks, J., J. S. Carson, B. L. Nelson and D. Nicol. 2005. Discrete-Event System Simulation. Fourth Edition. Prentice Hall, Inc., Upper Saddle River, NJ.
6. Barton, R. R., and M. Meckesheimer. 2006. Metamodel-based simulation optimization. Chapter 18 in Handbooks in Operations Research and Management Science: Simulation. North-Holland.
7. Batur, D., and S.-H. Kim. 2009. Finding feasible systems in the presence of constrains on multiple performance measures. ACM Transactions on Modeling and Computer Simulation, forthcoming.
8. Bechhofer, R. E. 1954. A single-sample multiple decision procedure for ranking means of normal populations with known variances. Annals of Mathematical Statistics 25:16-39.
9. Boesel, J., B. L. Nelson, and S.-H. Kim. 2003. Using ranking and selection to 'clean up' after simulation optimization. Operations Research 51:814-825.
10. Borkar, V. S. 2008. Stochastic Approximation: A Dynamic Systems Viewpoint. Cambridge University Press.
11. Branke, J., Chick, S., and Schmidt, C. 2007. Selecting a selection procedure. Management Science 53:1916-1932.
12. Chen, C.-H., J. Lin, E. Yücesan, and S. E. Chick. 2000. Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dynamic Systems: Theory and Applications 10:251-270.
13. Chick, S. E., and K. Inoue. 2001. New two-stage and sequential procedures for selecting the best simulated system. Operations Research 49:732-743. (Pubitemid 34011324)
14. Fabian, V. 1974. Note on Anderson's sequential procedures with triangular boundary. Annals of Statistics 2:170-176.
15. Fu, M. C. 2002. Optimization for simulation: Theory vs. practice. INFORMS Journal on Computing 14:192-215.
16. Fu, M. C. 2008. What you should know about simulation and derivatives. Naval Research Logistics 55:723-736.
17. Fu, M. C., and J. Q. Hu. 1997. Conditional Monte Carlo: Gradient Estimation and Optimization Applications, Kluwer Academic Publishers, Norwell, MA.
18. Glynn, P. W. 1990. Likelihood ratio gradient estimation for stochastic systems. Communications of the ACM 33:75-84.
19. Gong, W. B., Y. C. Ho, and W. Zhai. 1999. Stochastic comparison algorithm for discrete optimization with estimation. SIAM Journal on Optimization 10:384-404.
20. Ho, Y. C., and X.-R. Cao. 1983. Optimization and perturbation analysis of queueing networks. Journal of Optimization Theory and Applications 40:559-582. (Pubitemid 14449623)
21. Hong, L. J. 2009. Estimating quantile sensitivities. Operations Research 57: 118-130.
22. Hong, L. J., and G. Liu. 2009. Pathwise estimation of probability sensitivities through terminating and steady-state simulations. Operations Research, forthcoming.
23. Hong, L. J., and B. L. Nelson. 2005. The tradeoff between sampling and switching: New sequential procedures for indifference-zone selection. IIE Transactions 37:623-634.
24. Hong, L. J., and B. L. Nelson. 2006. Discrete Optimization via Simulation using COMPASS. Operations Research 54:115-129. (Pubitemid 43296745)
25. Hong, L. J., and B. L. Nelson. 2007a. A framework for locally convergent random search algorithms for discrete optimization via simulation. ACM Transactions on Modeling and Computer Simulation 17:19/1-19/22.
26. Hong, L. J., and B. L. Nelson. 2007b. Selecting the best system when systems are revealed sequentially. IIE Transactions 39:723-734.
27. Hu, J., M.C. Fu, and S.I. Marcus. 2007. A model reference adaptive search method for global optimization. Operations Research 55:549-568. (Pubitemid 351159509)
28. Kiefer, J., and Wolfowitz, J. 1952. Stochastic Estimation of the Maximum of a Regression Function. Annals of Mathematical Statistics 23:462-466.
29. Kim, S.-H. 2005. Comparison with a standard via fully sequential procedure. ACM Transactions on Modeling and Computer Simulation 15:1-20.
30. Kim, S.-H., and B. L. Nelson. 2001. A fully sequential procedure for indifference-zone selection in simulation. ACM Transactions on Modeling and Computer Simulation 11:251-273.
31. Kim, S.-H., and B. L. Nelson. 2006. On the asymptotic validity of fully sequential selection procedures for steady-state simulation. Operations Research 54:475-488. (Pubitemid 43838360)
32. Kim, S.-H., and B. L. Nelson. 2007. Recent Advances in Ranking and Selection. 2007 Winter Simulation Conference Proceedings, 162-172.
33. Kushner, H. J., and G. Yin. 2003. Stochastic approximation and recursive algorithms and applications, 2nd Edition. Springer.
34. Law, A. M. 2007. Simulation Modeling and Analysis, 4th Edition. McGraw-Hill.
35. Nelson, B. L., and F. J. Matejcik. 1995. Using Common Random Numbers for Indifference-Zone Selection and Multiple Comparisons in Simulation. Management Science 41:1935-1945.
36. Nelson, B. L., J. Swann, D. Goldsman, and W. Song. 2001. Simple procedures for selecting the best simulated system when the number of alternatives is large. Operations Research 49:950-963.
37. Olafsson, S.. 2006. Metaheuristics. Chapter 21 in Handbooks in Operations Research and Management Science: Simulation. North-Holland.
38. Paulson, E. 1964. A sequential procedure for selecting the population with the largest mean from k normal populations. Annals of Mathematical Statistics 35:174-180.
39. Pichitlamken, J., and B. L. Nelson. 2003. A combined procedure for optimization via simulation. ACM Transaction on Modeling and Computer Simulation 13:155-179.
40. Pichitlamken, J., B. L. Nelson, and L. J. Hong. 2006. A sequential procedure for neighborhood selection-of-the-best in optimization via simulation. European Journal of Operational Research 173:283-298.
41. Prudius, A. A., and S. Andradóttir. 2006. Balanced explorative and exploitative search with estimation for simulation optimization. Working Paper, School of ISyE, Georgia Tech, Atlanta, GA.
42. Reiman, M., and A. Weiss. 1989. Sensitivity analysis for simulations via likelihood ratios. Operations Research 37:830-844. (Pubitemid 20599548)
43. Rinott, Y. 1978. On two-stage selection procedures and related probability-inequalities. Communications in Statistics A7:799-811.
44. Robbins, H., and S. Monro. 1951. A Stochastic Approximation Method. Annals of Mathematical Statistics 22:400-407.
45. Shi, L., and S. Ólafsson. 2000. Nested partitions method for stochastic optimization. Methodology and Computing in Applied Probability 2:271-291. (Pubitemid 33210831)
46. Spall, J. C. 1992. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control 37:332-341.
47. Spall, J. C. 1998. An overview of the simultaneous perturbation method for efficient optimization. Johns Hopkins APL Technical Digest 19:482-492. (Pubitemid 128568779)
48. Xu, J., B. L. Nelson, and L. J. Hong. 2009. Industrial Strength COMPASS: A Comprehensive Algorithm and Software for Optimization via Simulation. ACM Transactions on Modeling and Computer Simulation, forthcoming.
49. Yan, D., and H. Mukai. 1992. Stochastic discrete optimization. SIAM Journal of Control and Optimization 30:594-612.