Stochastic Programming Models: Wait-and-See Versus Here-and-Now

  • Roger J-B. Wets
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 128)

Abstract

We introduce a number of stochastic programming models via examples and then proceed to derive one of the fundamental theorems in the field that brings to the fore the constrast between wait-and-see and here-and-now formulations.

Key words

Stochastic programming wait-and-see here-and-now measurable mappings nonanticipativity 

AMS(MOS) subject classifications

90C15 

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References

  1. [1]
    T. Helgason and S.W. Wallace. Approximate scenario solution in the progressive hedging algorithm. RH-08–89, Raumvísindastofnun Háskólans, 1989.Google Scholar
  2. [2]
    J.M. Mulvey and A. Ruszczynski. A new scenario decomposition method for large-scale stochastic optimization. Operations Research, 43:477–490, 1995.MathSciNetMATHCrossRefGoogle Scholar
  3. [3]
    J.M. Mulvey and H. Vladimirou. Evaluation of a distributed hedging algorithm for stochastic network programming. Statistics and Operations Research SOR 88–14, Princeton University, 1988.MATHGoogle Scholar
  4. [4]
    J.M. Mulvey and H. Vladimirou. Solving multistage investment problems: an application of scenario aggregation. Statistics and Operations Research SOR 88–1, Princeton University, 1988.Google Scholar
  5. [5]
    R.T. Rockafellar and R.J-B Wets. Scenarios and policy aggregation in optimization under uncertainty. Mathematics of Operations Research, 16:119–147, 1991.MathSciNetMATHCrossRefGoogle Scholar
  6. [6]
    R.T. Rockafellar and R.J-B Wets. Variational Analysis. Springer, 1998.MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York, Inc. 2002

Authors and Affiliations

  • Roger J-B. Wets
    • 1
  1. 1.Department of MathematicsUniversity of California, DavisDavisUSA

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