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An Introductory Tutorial on Stochastic Linear Programming Models

Author

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  • Suvrajeet Sen

    (Department of Systems and Industrial Engineering, The University of Arizona, Tucson, Arizona 85721)

  • Julia L. Higle

    (Department of Systems and Industrial Engineering, The University of Arizona, Tucson, Arizona 85721)

Abstract

Linear programming is a fundamental planning tool. It is often difficult to precisely estimate or forecast certain critical data elements of the linear program. In such cases, it is necessary to address the impact of uncertainty during the planning process. We discuss a variety of LP-based models that can be used for planning under uncertainty. In all cases, we begin with a deterministic LP model and show how it can be adapted to include the impact of uncertainty. We present models that range from simple recourse policies to more general two-stage and multistage SLP formulations. We also include a discussion of probabilistic constraints. We illustrate the various models using examples taken from the literature. The examples involve models developed for airline yield management, telecommunications, flood control, and production planning.

Suggested Citation

  • Suvrajeet Sen & Julia L. Higle, 1999. "An Introductory Tutorial on Stochastic Linear Programming Models," Interfaces, INFORMS, vol. 29(2), pages 33-61, April.
    • Handle: RePEc:inm:orinte:v:29:y:1999:i:2:p:33-61
    DOI: 10.1287/inte.29.2.33
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    References listed on IDEAS

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    1. David R. Cariño & Terry Kent & David H. Myers & Celine Stacy & Mike Sylvanus & Andrew L. Turner & Kouji Watanabe & William T. Ziemba, 1994. "The Russell-Yasuda Kasai Model: An Asset/Liability Model for a Japanese Insurance Company Using Multistage Stochastic Programming," Interfaces, INFORMS, vol. 24(1), pages 29-49, February.
    2. M. I. Kusy & W. T. Ziemba, 1986. "A Bank Asset and Liability Management Model," Operations Research, INFORMS, vol. 34(3), pages 356-376, June.
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