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Stochastic Programming and Robust Optimization

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Advances in Sensitivity Analysis and Parametic Programming

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 6))

Abstract

Earlier chapters dealt with problems arising from data uncertainty by examining the sensitivity of the model’s recommendations with respect to changes in the data. This chapter presents prescriptive approaches to problems of sensitivity analysis and parametric programming. It provides an introduction to stochastic programming and robust optimization models. Such models deal, in a constructive manner, with noisy, incomplete or uncertain data. Information about possible values of the problem data is incorporated in the model, and the model generates solutions that are less sensitive to data uncertainty. Stochastic linear programming and robust optimization models are introduced and applications are presented, with emphasis on financial planning problems.

The material in this chapter draws extensively from Chapter 13 of the book by Yair Censor and Stavros A. Zenios, Parallel Optimization: Theory, Algorithms and Applications, to be published in The Oxford Series in Numerical Analysis, by Oxford University Press.

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Authors
  1. Hercules Vladimirou
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  2. Stavros A. Zenios
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Editors and Affiliations

  1. FernUniversität, Hagen, Germany

    Tomas Gal

  2. University of Colorado at Denver, USA

    Harvey J. Greenberg

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Vladimirou, H., Zenios, S.A. (1997). Stochastic Programming and Robust Optimization. In: Gal, T., Greenberg, H.J. (eds) Advances in Sensitivity Analysis and Parametic Programming. International Series in Operations Research & Management Science, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6103-3_12

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  • DOI: https://doi.org/10.1007/978-1-4615-6103-3_12

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