Advertisement

Discrete Optimization

  • Urmila M. Diwekar
Part of the Applied Optimization book series (APOP, volume 80)

Abstract

Discrete optimization problems involve discrete decision variables as shown below in Example 4.1.

Keywords

Genetic Algorithm Simulated Annealing Mixed Integer Linear Programming Master Problem Discrete Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. 1.
    Ahuja, R. K., J. B. Orlin (1997), Developing fitter Genetic Algorithms, INFORMS Journal of Computing, 9 (3), 251.CrossRefGoogle Scholar
  2. 2.
    Beale E. M. (1977), Integer Programming: The State of the Art in Numerical Analysis, Academic Press, London.Google Scholar
  3. 3.
    Biegler L., I. E. Grossmann, and A. W. Westerberg (1997), Systematic Methods of Chemical Process Design, Prentice Hall International, Upper Saddle River, NJ.Google Scholar
  4. 4.
    Chiba, T., Okado, S., and I. Fujii (1996), Optimum support arrangement of piping systems using genetic algorithm, Journal of Pressure Vessel Technology, 118, 507.CrossRefGoogle Scholar
  5. 5.
    Collins N. E., R. W. Eglese, and B. L. Golden (1988), Simulated Annealing — An annotated biography, American Journal of Mathematical and Management Science, 8 (3), 209.MathSciNetMATHGoogle Scholar
  6. 6.
    Diwekar U. M., I. E. Grossmann, and E. S. Rubin (1991), An MINLP process synthesizer for a sequential modular simulator, Industrial and Engineering Chemistry Research, 31, 313.CrossRefGoogle Scholar
  7. 7.
    Dunn, S.A. (1997), Modified genetic algorithm for the identification of aircraft structures, Journal of Aircraft, 34, 251.CrossRefGoogle Scholar
  8. 8.
    Glover F. (1986), Future paths for integer programming and links to artificial intelligence, Computers and Operations Research, 5, 533.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Goldberg D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley , Reading MA.MATHGoogle Scholar
  10. 10.
    Guarnieri, F. and M. Mezei (1996), Simulated annealing of chemical potential: A General procedure for locating bound waters. Application to the study of the differential hydration propensities of the major and minor grooves of DNA, Journal of the American Chemical Society, 118, 8493.CrossRefGoogle Scholar
  11. 11.
    Hendry J. E. and R. R. Hughes (1972), Generating separation flowsheets, Chemical Engineering Progress, 68, 69.Google Scholar
  12. 12.
    Holland J.H. (1975), Adaptation in Natural and Artificial Systems, Uni-versity of Michigan Press, Ann Arbor.Google Scholar
  13. 13.
    Holland J.H. (1992), Genetic Algorithms, Scientific American, July, 66.Google Scholar
  14. 14.
    Huang M. D., F. Romeo, and A. L. Sangiovanni-Vincetelli (1986), An efficient general cooling schedule for Simulated Annealing, Proceedings of IEEE Conference on Computer Design, 381.Google Scholar
  15. 15.
    Kershenbaum A. (1997), When Genetic Algorithms work best, INFORMS Journal of Computing, 9 (3), 254.CrossRefGoogle Scholar
  16. 16.
    Kirkpatrick S., C. Gelatt, and M. Vecchi (1983), Optimization by Simulated Annealing, Science, 220 (4598), 670.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Lettau, M. (1997), Explaining the facts with adaptive agents: The case of mutual fund flows, Journal of Economic Dynamics and Control, 21 (7), 1117.MATHCrossRefGoogle Scholar
  18. 18.
    Levine D. (1997), Genetic Algorithms: A practitioner’s view, INFORMS Journal of Computing, 9 (3), 256.CrossRefGoogle Scholar
  19. 19.
    Narayan, V., Diwekar U.M. and Hoza M. (1996), Synthesizing optimal waste blends, Industrial and Engineering Chemistry Research, 35, 3519.CrossRefGoogle Scholar
  20. 20.
    Painton L. and U. M. Diwekar (1994), Synthesizing optimal design configurations for a Brayton cycle power plant, Computers & chemical Engineering, 18, 369.CrossRefGoogle Scholar
  21. 21.
    Price T.C. (1997), Using co-evolutionary programming to simulate strategic behavior in markets, Journal of Evolutionary Economics, 7 (3), 219.CrossRefGoogle Scholar
  22. 22.
    Reeves C.R. (1997), Genetic Algorithms: No panacea, but a valuable tool for the operations researcher, INFORMS Journal of Computing, 9 (3), 263.CrossRefGoogle Scholar
  23. 23.
    Ross P. (1997), What are Genetic Algorithms good at?, INFORMS Journal of Computing, 9 (3), 260.CrossRefGoogle Scholar
  24. 24.
    Taha H. A. (1997), Operations Research: An Introduction, Sixth Edition, Prentice Hall , Upper Saddle River, NJ.MATHGoogle Scholar
  25. 25.
    Winston W. L. (1991), Operations Research: Applications and Algorithms, Second Edition, PWS-KENT Co., Boston, MA.MATHGoogle Scholar
  26. 26.
    Van Laarhoven P. J. M. and E. H. Aarts (1987), Simulated Annealing Theory and Applications, D. Reidel Publishing Co: Holland.MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Urmila M. Diwekar
    • 1
  1. 1.Center for Uncertain Systems: Tools for Optimization & Management, Department of Chemical Engineering, and Institute for Environmental Science & PolicyUniversity of Illinois at ChicagoChicagoUSA

Personalised recommendations