Stochastic approximation with simulated annealing as an approach to global discrete-event simulation optimization

Proceedings - Winter Simulation Conference

Volumn 1, Issue , 2004, Pages 500-507

  Jones, Matthew H ^a   White Jr , K Preston ^a  

^a University of Virginia   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; ESTIMATION; OPTIMIZATION; PROBABILITY; ROBUSTNESS (CONTROL SYSTEMS); SIMULATED ANNEALING;

APPROXIMATE GRADIENT INFORMATION; DISCRETE-EVENT SIMULATION OPTIMIZATION; LOCAL OPTIMA; STOCHASTIC APPROXIMATION;

RANDOM PROCESSES;

EID: 17744367658     PISSN: 08917736     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Elder, J. D. 1993. Efficient optimization through response surface modeling: a GROPE algorithm. Doctoral dissertation, Department of Systems Engineering, University of Virginia, Charlottesville, Virginia.
2. Fang, H. G., and M. Qian. 1997. Annealing of iterative stochastic schemes. SIAM Journal on Applied Mathematics 35: 1886-1907.
3. Fu, M. C., and S. D. Hill. 1997. Optimization of discrete event systems via simultaneous perturbation stochastic approximation. IIE Transactions 29: 233-243.
4. Fu, M. C. 2002. Optimization for simulation: theory vs. practice. INFORMS Journal on Computing 14: 192-215.
5. Garai, I., Y. C. Ho, and R. S. Sreenivas. 1992. Hybrid optimization-an experimental study. In Proceedings of the 31st IEEE Conference on Decision and Control 2: 2068-2073.
6. d. SIAM Journal on Applied Mathematics 31: 111-131.
7. Goldsman, D., and B. L. Nelson. 2001. Statistical selection of the best system. In Proceedings of the 2001 Winter Simulation Conference, ed. B. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer, 139-146. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.
8. Glover, F. K., and M. Laguna. 2000. The OptQuest approach to crystal ball simulation optimization [online]. Available online via 〈http://www. crystalball.com/optquest/paperlist.html〉
9. Kushner, H. J. 1987. Asymptotic global behaviour for stochastic approximation and diffusions with slowly decreasing noise effects: global minimization via Monte Carlo. SIAM Journal on Applied Mathematics 47: 169-185.
10. Laguna, M. 1997a. Optimization of complex systems with OptQuest [online]. Available online via 〈http://www.crystalball.com/optquest/paperlist. html〉
11. Laguna, M. 1997b. Metaheuristic optimization with Evolver, Genocop and OptQuest [online]. Available online via 〈http://www.crystalball.com/ optquest/paperlist.html〉
12. rd ed. New York: McGraw Hill.
13. 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.
14. Robbins, H., and S. Monro. 1951. A Stochastic approximation method. Annals of Mathematical Statistics 22: 400-407.
15. Spall, J. C. 1998. Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Transactions on Aerospace and Electronic Systems 34: 817-823.
16. Spall, J. C. 2003. Introduction to stochastic search and optimization. Hoboken, New Jersey: John Wiley & Sons, Inc.
17. Swisher, J. R. J., and S. H. Jacobson. 1999. A survey of ranking, selection, and multiple comparison procedures for discrete-event simulation. In Proceedings of the 1999 Winter Simulation Conference, ed. P. A. Farrington, H. B. Nembhard, D. T. Sturrock, and G. W. Evans, 492-501. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers.
18. Yin, G. 1999. Rates of convergence for a class of global stochastic optimization algorithms. SIAM Journal on Applied Mathematics 10: 99-120.