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The Age of Optimization: Solving Large-Scale Real-World Problems

Author

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  • George L. Nemhauser

    (Georgia Institute of Technology, Atlanta, Georgia)

Abstract

In the last decade, new advances in algorithms have been as important as the impressive advances in computer technology. Using the new interior-point algorithms and advanced implementations of simplex methods, we can now solve linear programs with more than one million variables and thousands of constraints. Preprocessing and polyhedral theory have yielded at least an order of magnitude improvement in branch-and-bound algorithms for solving mixed integer programs. Moreover, these algorithmic advances have been incorporated in commercially inexpensive software that is readily available, easily portable, and supported by a variety of systems that make it possible for unsophisticated users to input and check their models and obtain understandable outputs. This paper, based on the Morse Lecture given in May 1993 at the TIMS/ORSA meeting in Chicago, begins with some of the modem history of optimization, then surveys some recent developments (illustrating them with an application in the airline industry), and closes with some remarks about the future.

Suggested Citation

  • George L. Nemhauser, 1994. "The Age of Optimization: Solving Large-Scale Real-World Problems," Operations Research, INFORMS, vol. 42(1), pages 5-13, February.
    • Handle: RePEc:inm:oropre:v:42:y:1994:i:1:p:5-13
    DOI: 10.1287/opre.42.1.5
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    Cited by:

    1. Luss, Hanan & Rosenwein, Moshe B., 1997. "Operations Research applications: Opportunities and accomplishments," European Journal of Operational Research, Elsevier, vol. 97(2), pages 220-244, March.
    2. Di Giorgio, Alessandro & Pimpinella, Laura, 2012. "An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management," Applied Energy, Elsevier, vol. 96(C), pages 92-103.
    3. Ali, Agha Iqbal & Han, Hyun-Soo, 1998. "Computational implementation of Fujishige's graph realizability algorithm," European Journal of Operational Research, Elsevier, vol. 108(2), pages 452-463, July.
    4. Diane E. Vaughan & Sheldon H. Jacobson, 2004. "Tabu Guided Generalized Hill Climbing Algorithms," Methodology and Computing in Applied Probability, Springer, vol. 6(3), pages 343-354, September.

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