Efficient computing budget allocation for simulation-based policy improvement

ASCC 2011 - 8th Asian Control Conference - Final Program and Proceedings

Volumn , Issue , 2011, Pages 920-925

  Jia, Qing Shan ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Discrete event dynamic system; optimal computing budget allocation; simulation based policy improvement

Indexed keywords

ALLOCATION PROBLEMS; COMPUTING BUDGET; DISCRETE EVENT DYNAMIC SYSTEMS; GENERAL SITUATION; HIGH PROBABILITY; LOWER BOUNDS; NUMERICAL EXAMPLE; NUMERICAL RESULTS; OPTIMAL COMPUTING BUDGET ALLOCATION; PERFORMANCE EVALUATION; PHYSICAL LAWS; POLICY OPTIMIZATION; SIMULATION-BASED; SIMULATION-BASED OPTIMIZATIONS;

DYNAMICAL SYSTEMS; OPTIMIZATION;

BUDGET CONTROL;

EID: 80051989557     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. C. G. Cassandras and S. Lafortune, Introduction to Discrete Event Systems, 2nd ed. New York, NY: Springer, 2008.
2. C.-H. Chen, J. Lin, E. Yücesan, and S. E. Chick, "Simulation budget allocation for further enhancing the efficiency of ordinal optimization," Discrete Event Dynamic Systems: Theory and Applications, vol. 10, pp. 251-270, 2000.
3. C. H. Chen and E. Yucesan, "An alternative simulation budget allocation scheme for efficient simulation," International Journal of Simulation and Process Modeling, vol. 1, pp. 49-57, 2005.
4. M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York, NY: John Wiley and Sons, Inc., 1994.
5. D. P. Bertsekas, Dynamic Programming and Optimal Control, 3rd ed. Belmont, MA: Athena Scientific, 2007, vol. 1 and 2.
6. D. P. Bertsekas and J. N. Tsitsiklis, Neuro-Dynamic Programming. Belmont, MA: Athena Scientific, 1996.
7. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press, 1998.
8. W. B. Powell, Approximate Dynamic Programming: Solving the Curse of Dimensionality. Wiley-Interscience, 2007.
9. X. R. Cao, Stochastic Learning and Optimization: A Sensitivity-Based Approach. New York, NY: Springer, 2007.
10. A. Gosavi, "Reinforcement learning: A tutorial survey and recent advances," pp. 178-192, 2009.
11. T. Jaakola, M. Jordan, and S. P. Singh, "On the convergence of stochastic iterative dynamic programming algorithms," Neural Computation, vol. 6, pp. 1185-1201, 1994.
12. J. N. Tsitsiklis, "Asynchronous stochastic approximation and qlearning," Machine Learning, vol. 16, pp. 185-202, 1994.
13. V. S. Borkar, "Asynchronous stochastic approximations," SIAM Journal on Control and Optimization, vol. 36, no. 3, pp. 840-851, 1998. (Pubitemid 128493576)
14. V. S. Borkar and S. P. Meyn, "The ode method for convergence of stochastic approximation and reinforcement learning," SIAM Journal on Control and Optimization, vol. 38, no. 2, pp. 447-469, 2000. (Pubitemid 30594546)
15. J. N. Tsitsiklis and B. van Roy, "Feature-based methods for large-scale dynamic programming," Machine Learning, vol. 22, pp. 59-94, 1996. (Pubitemid 126724363)
16. R. E. Bechhofer, "A single-sample multiple decision procedure for ranking means of normal populations with known variances," Annals of Mathematical Statistics, vol. 25, pp. 16-39, 1954.
17. S. S. Gupta, "On a decision rule for a problem in ranking means," Ph.D. dissertation, Institute of Statistics, University of North Carolina, Chapel Hill, NC, 1956.
18. -, "On some multiple decision (ranking and selection) rules," Technometrics, vol. 7, pp. 225-245, 1965.
19. R. E. Bechhofer, T. J. Santner, and D. Goldsman, Design and Analysis of Experiments for Statistical Selection, Screening and Multiple Comparisons, New York, NY: John Wiley & Sons, 1995.
20. J. R. Swisher, S. H. Jacobson, and E. Yücesan, "Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey," ACM Transactions on Modeling and Computer Simulation, vol. 13, pp. 134-154, 2003.
21. S.-H. Kim and B. L. Nelson, "Selecting the best system: Theory and methods," in Proceedings of the 2003 Winter Simulation Conference, S. Chick, P. J. Sánchez, D. Ferrin, and D. J. Morrice, Eds., Piscataway, New Jersey, 2003, pp. 101-112.
22. J. M. Bernardo and A. F. M. Smith, "Bayesian theory," 1994.
23. K. Inoue and S. E. Chick, "Comparison of bayesian and frequentist assessment of uncertainty for selecting the best system," in Proceedings of the 1998 Winter Simulation Conference, D. J. Medeiros, E. F. Watson, J. S. Carson, and M. S. Manivannan, Eds. Piscataway, NJ: IEEE, 1998, pp. 727-734.
24. C.-H. Chen, D. He, M. Fu, and L. H. Lee, "Efficient simulation budget allocation for selecting an optimal subset," INFORMS Journal on Computing, vol. 20, no. 4, pp. 579-595, 2008.
25. N. A. Pujowidianto, L. H. Lee, C.-H. Chen, and C. M. Yap, "Optimal computing budget allocation for constrained optimization," in Proceedings of the 2009 Winter Simulation Conference, M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls, Eds., 2009, pp. 584-589.
26. L. H. Lee, E. P. Chew, S. Teng, and D. Goldsman, "Optimal computing budget allocation for multi-objective simulation models," in Proceedings of the 2004 Winter Simulation Conference, R. G. Ingalls, M. D. Rosetti, J. S. Smith, and B. A. Peters, Eds. Piscataway, NJ: Institute of Electrical and Electronics Engineers, Inc., 2004, pp. 586-594.
27. M. C. Fu, J. H. Hu, C. H. Chen, and X. Xiong, "Simulation allocation for determining the best design in the presence of correlated sampling," INFORMS Journal on Computing, vol. 19, no. 1, pp. 101-111, 2007.
28. S. Yan, E. Zhou, and C.-H. Chen, "Efficient simulation budget allocation for selecting the best set of simplest good enough designs," in Proceedings of the 2010 Winter Simulation Conference, B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, Eds., 2010.
29. C. H. Chen and L. H. Lee, Stochastic Simulation Optimization: An Optimal Computing Budget Allocation. World Scientific Publishing Co., 2010.
30. D. P. Bertsekas and D. A. Castanon, "Rollout algorithms for stochastic scheduling problems," Journal of Heuristics, vol. 5, pp. 89-108, 1999.
31. M. H. DeGroot, Optimal Statistical Decisions. New York, NY: McGraw-Hill, 1970.
32. A. M. Law and W. D. Kelton, "Simulation modeling & analysis," 1991.