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Part of the book series: Applied Optimization ((APOP,volume 80))

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Abstract

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

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Bibliography

  1. Ahuja, R. K., J. B. Orlin (1997), Developing fitter Genetic Algorithms, INFORMS Journal of Computing, 9 (3), 251.

    Article  Google Scholar 

  2. Beale E. M. (1977), Integer Programming: The State of the Art in Numerical Analysis, Academic Press, London.

    Google Scholar 

  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. Chiba, T., Okado, S., and I. Fujii (1996), Optimum support arrangement of piping systems using genetic algorithm, Journal of Pressure Vessel Technology, 118, 507.

    Article  Google Scholar 

  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.

    MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  7. Dunn, S.A. (1997), Modified genetic algorithm for the identification of aircraft structures, Journal of Aircraft, 34, 251.

    Article  Google Scholar 

  8. Glover F. (1986), Future paths for integer programming and links to artificial intelligence, Computers and Operations Research, 5, 533.

    Article  MathSciNet  Google Scholar 

  9. Goldberg D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley , Reading MA.

    MATH  Google Scholar 

  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.

    Article  Google Scholar 

  11. Hendry J. E. and R. R. Hughes (1972), Generating separation flowsheets, Chemical Engineering Progress, 68, 69.

    Google Scholar 

  12. Holland J.H. (1975), Adaptation in Natural and Artificial Systems, Uni-versity of Michigan Press, Ann Arbor.

    Google Scholar 

  13. Holland J.H. (1992), Genetic Algorithms, Scientific American, July, 66.

    Google Scholar 

  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. Kershenbaum A. (1997), When Genetic Algorithms work best, INFORMS Journal of Computing, 9 (3), 254.

    Article  Google Scholar 

  16. Kirkpatrick S., C. Gelatt, and M. Vecchi (1983), Optimization by Simulated Annealing, Science, 220 (4598), 670.

    Article  MathSciNet  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  18. Levine D. (1997), Genetic Algorithms: A practitioner’s view, INFORMS Journal of Computing, 9 (3), 256.

    Article  Google Scholar 

  19. Narayan, V., Diwekar U.M. and Hoza M. (1996), Synthesizing optimal waste blends, Industrial and Engineering Chemistry Research, 35, 3519.

    Article  Google Scholar 

  20. Painton L. and U. M. Diwekar (1994), Synthesizing optimal design configurations for a Brayton cycle power plant, Computers & chemical Engineering, 18, 369.

    Article  Google Scholar 

  21. Price T.C. (1997), Using co-evolutionary programming to simulate strategic behavior in markets, Journal of Evolutionary Economics, 7 (3), 219.

    Article  Google Scholar 

  22. Reeves C.R. (1997), Genetic Algorithms: No panacea, but a valuable tool for the operations researcher, INFORMS Journal of Computing, 9 (3), 263.

    Article  Google Scholar 

  23. Ross P. (1997), What are Genetic Algorithms good at?, INFORMS Journal of Computing, 9 (3), 260.

    Article  Google Scholar 

  24. Taha H. A. (1997), Operations Research: An Introduction, Sixth Edition, Prentice Hall , Upper Saddle River, NJ.

    MATH  Google Scholar 

  25. Winston W. L. (1991), Operations Research: Applications and Algorithms, Second Edition, PWS-KENT Co., Boston, MA.

    MATH  Google Scholar 

  26. Van Laarhoven P. J. M. and E. H. Aarts (1987), Simulated Annealing Theory and Applications, D. Reidel Publishing Co: Holland.

    Book  MATH  Google Scholar 

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© 2003 Springer Science+Business Media New York

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Diwekar, U.M. (2003). Discrete Optimization. In: Introduction to Applied Optimization. Applied Optimization, vol 80. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3745-5_4

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  • DOI: https://doi.org/10.1007/978-1-4757-3745-5_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4757-3747-9

  • Online ISBN: 978-1-4757-3745-5

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