Skip to main content
Log in

Performance Optimization of a Class of Discrete Event Dynamic Systems Using Calculus of Variations Techniques

  • Published:
Journal of Optimization Theory and Applications Aims and scope Submit manuscript

Abstract

We explore an approach involving the use of calculus of variations techniques for discrete event dynamic system (DEDS) performance optimization problems. The approach is motivated by the observation that such problems can be described by separable cost functions and recursive dynamics of the same form as that used to describe conventional discrete-time continuous-variable optimal control problems. Three important difficulties are that DEDS are generally stochastic, their dynamics typically involve max and min operations, which are not everywhere differentiable, and the state variables are often discrete. We demonstrate how to overcome these difficulties by applying the approach to a transportation problem, modeled as a polling system, where we are able to derive an explicit and intuitive analytic expression for an optimal control policy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Ho, Y. C., and Cao, X. R., Perturbation Analysis of Discrete Event Dynamic Systems, Kluwer Academic Publishers, Boston, Massachusetts, 1991.

    Google Scholar 

  2. Cassandras, C. G., and Panayiotou, C., Concurrent Sample Path Estimation for Discrete Event Systems, Proceedings of the 35th IEEE Conference on Decision and Control, pp. 3332–3337, 1996.

  3. Rubinstein, R. Y., and Shapiro, A., Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method, John Wiley and Sons, New York, New York, 1993.

    Google Scholar 

  4. Ho, Y. C., Overview of Ordinal Optimization, Proceedings of the 33rd IEEE Conference on Decision and Control, pp. 1975–1977, 1993.

  5. Ho, Y. C., and Deng, M., The Problem of Large Search Space in Stochastic Optimization, Proceedings of the 33rd IEEE Conference on Decision and Control, pp. 1470–1475, 1993.

  6. Bryson, A. E. Jr., and Ho, Y. C., Applied Optimal Control: Optimization, Estimation, and Control, Hemisphere Publishing Company, Washington, DC, 1975.

    Google Scholar 

  7. Sage, A. P., and White, C. C., III, Optimum Systems Control, 2nd Edition, Prentice-Hall, Englewood Cliffs, New Jersey, 1977.

    Google Scholar 

  8. Whittle, P., Optimization over Time: Dynamic Programming and Stochastic Control, Vol. 1, John Wiley and Sons, New York, New York, 1982.

    Google Scholar 

  9. Whittle, P., Optimization over Time: Dynamic Programming and Stochastic Control, Vol. 2, John Wiley and Sons, New York, New York, 1983.

    Google Scholar 

  10. Whittle, P., Optimal Control: Basics and Beyond, John Wiley and Sons, New York, New York, 1996.

    Google Scholar 

  11. Gazarik, M., and Wardi, Y., Optimal Release Times in a Single Server: An Optimal Control Perspective, Proceedings of the 35th IEEE Conference on Decision and Control, pp. 3831–3836, 1996.

  12. Pepyne, D. L., and Cassandras, C. G., Modeling, Analysis, and Optimal Control of a Class of Hybrid Systems, Journal of Discrete Event Dynamic Systems, Vol. 8, pp. 175–201, 1998.

    Google Scholar 

  13. Clarke, F. H., Methods of Dynamic and Nonsmooth Optimization, SIAM, Philadelphia, Pennsylvania, 1989.

    Google Scholar 

  14. Clarke, F. H., Optimization and Nonsmooth Analysis, Wiley-Interscience, New York, New York, 1983.

    Google Scholar 

  15. Rockafellar, R. T., The Theory of Subgradients and Its Application to Problems of Optimization: Convex and Nonconvex Functions, Heldermann Verlag, Berlin, Germany, 1981.

    Google Scholar 

  16. Garfinkel, R. S., and Nemhauser, G. L., Integer Programming, John Wiley and Sons, New York, New York, 1972.

    Google Scholar 

  17. Minoux, M., Mathematical Programming: Theory and Algorithms, John Wiley and Sons, New York, New York, 1986.

    Google Scholar 

  18. Taha, H. A., Integer Programming: Theory, Applications, and Computations, Academic Press, New York, New York, 1975.

    Google Scholar 

  19. Baccelli, F., Cohen, G., Olsder, G. J., and Quadrat, J. P., Synchronization and Linearity, John Wiley and Sons, New York, New York, 1992.

    Google Scholar 

  20. Cassandras, C. G., Discrete Event Systems: Modeling and Performance Analysis, Irwin and Aksen, Boston, Massachusetts, 1993.

    Google Scholar 

  21. Cassandras, C. G., Lafortune, S., and Olsder, G. J., Introduction to the Modelling, Control, and Optimization of Discrete Event Systems, Trends in Control, Edited by A. Isidori, Springer Verlag, Berlin, Germany, pp. 217–292, 1995.

    Google Scholar 

  22. Kleinrock, L., Queueing Systems, Vol. 1: Theory, Wiley-Interscience, New York, New York, 1975.

    Google Scholar 

  23. Murata, T., Petri Nets: Properties, Analysis, and Applications, Proceedings of the IEEE, Vol. 77, pp. 541–580, 1989.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pepyne, D.L., Cassandras, C.G. Performance Optimization of a Class of Discrete Event Dynamic Systems Using Calculus of Variations Techniques. Journal of Optimization Theory and Applications 100, 599–622 (1999). https://doi.org/10.1023/A:1022690507461

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1022690507461

Navigation