Mathematical programming-based perturbation analysis for GI/G/1 queues

Proceedings - Winter Simulation Conference

Volumn , Issue , 2007, Pages 553-559

  Zhang, He ^a   Chan, Wai Kin ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMIC PROGRAMMING; LINEAR PROGRAMMING; LINEARIZATION; MATHEMATICAL PROGRAMMING; PERTURBATION TECHNIQUES;

DISCRETE-EVENT DYNAMIC SYSTEMS; INFINITESIMAL PERTURBATION ANALYSIS; LINEAR PROGRAMMING TECHNIQUES; PAPER ADDRESSES; PERTURBATION ANALYSIS; UPPER BOUNDS;

ERROR ANALYSIS;

EID: 49749108222     PISSN: 08917736     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WSC.2007.4419647     Document Type: Conference Paper

References (18)

1. Cao, X. R. 1985. Convergence of parameter sensitivity estimates in a stochastic experiment. IEEE Transactions on Automatic Control, 30(9):845-853.
2. Chan, W. K. V. and L. W. Schruben. 2003. Properties of discrete event system from their mathematical programming representations. In Proceeding of the 2003 Winter Simulation Conference, ed. S. E. Chick, P. J. Sanchez, D. Ferrin, and D. J. Morrice, 496-502. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc..
3. Chan, W. K. V. 2005. Mathematical representation of discrete-event system dynamics. Ph.D dissertation, University of California, Berkeley.
4. Dudley, R. M. 2002. Real analysis and probability. 2nd ed. Cambridge, England: Cambridge University Express.
5. Freimer, M. and L. W. Schruben. 2001. Graphical representation of IPA estimation. In Proceeding of the 2001 Winter Simulation Conference, ed. Peter, B. A., Smith, J. S., Medeiros, D. J. and Rohrer, M. W. 422-427. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc..
6. Fu, M. C. and J. Q. Hu. 1991. Consistency of infinitesimal perturbation analysis for the GI/G/m queue. European Journal of Operations Research, 34(1):39-64.
7. Fu, M. and J. Q. Hu. 1997. Conditional monte carlo: gradient estimation and optimization applications. Boston, Massachusetts: Kluwer Academic Publishers.
8. st Ed. New York, USA: McGraw Hill.
9. Gal, T. and H. J. Greenberg. 1997. Advances in sensitivity analysis and parametric programming. Boston, Massachusetts: Kluwer Academic Publishers.
10. Glasserman, P. and Y. C. Ho. 1991. Gradient estimation via perturbation analysis. Boston, Massachusetts: Kluwer Academic Publishers.
11. Heidelberger, P., X. R. Cao, M. A. Zazanis and R. Suri 1988. Convergence properties of infinitesimal perturbation analysis estimates. Management Science, 34(11):1281-1302.
12. Ho, Y. C. and X. R. Cao. 1991. Perturbation analysis of discrete event dynamic systems. Boston, Massachusetts: Kluwer Academic Publishers.
13. Renegar, J. 1994. Some perturbation theory of linear programming. Mathematical Programming, 65(1-3):73-91.
14. Rockafellar, R. T. 1970. Convex Analysis. Princeton, New Jersey: Princeton University Press.
15. Schruben, L. W. 2000. Mathematical Programming Models of Discrete Event System Dynamics. Proceedings of the 2000 Winter Simulation Conference Proceedings (Cat. NO. 00CH37165). IEEE. Part vol. 1, 2000, pp. 381-385. Piscataway, NJ, USA.
16. Suri, R. and M. A. Zazanis. 1988. Perturbation analysis gives strongly consistent sensitivity estimates for the M/G/1 queue. Management Science, 34(1):39-64.
17. Ward, J. E. 1990. Approaches to sensitivity analysis in linear programming. Annals of Operations Research, 27(1):3-38.
18. th Allerton Conference on Communication, Computing and Control:297-304.