Skip to main content
SpringerLink
Log in
Book cover

Decision Making Under Uncertainty pp 1–15Cite as

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

  • Conference paper

Part of the book series: The IMA Volumes in Mathematics and its Applications ((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.

Research supported in part by a grant of the National Science Foundation.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. T. Helgason and S.W. Wallace. Approximate scenario solution in the progressive hedging algorithm. RH-08–89, Raumvísindastofnun Háskólans, 1989.

    Google Scholar 

  2. J.M. Mulvey and A. Ruszczynski. A new scenario decomposition method for large-scale stochastic optimization. Operations Research, 43:477–490, 1995.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    MATH  Google Scholar 

  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. R.T. Rockafellar and R.J-B Wets. Scenarios and policy aggregation in optimization under uncertainty. Mathematics of Operations Research, 16:119–147, 1991.

    Article  MathSciNet  MATH  Google Scholar 

  6. R.T. Rockafellar and R.J-B Wets. Variational Analysis. Springer, 1998.

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, University of California, Davis, Davis, CA, 95616, USA

    Roger J-B. Wets

Authors
  1. Roger J-B. Wets
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. IBM Watson Research Center, Hawthorne, NY, 10532, USA

    Claude Greengard

  2. Department of Management Science and Information Systems, Rutgers University, Piscataway, NJ, 08854, USA

    Andrzej Ruszczynski

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag New York, Inc.

About this paper

Cite this paper

Wets, R.JB. (2002). Stochastic Programming Models: Wait-and-See Versus Here-and-Now. In: Greengard, C., Ruszczynski, A. (eds) Decision Making Under Uncertainty. The IMA Volumes in Mathematics and its Applications, vol 128. Springer, New York, NY. https://doi.org/10.1007/978-1-4684-9256-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4684-9256-9_1

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-3014-9

  • Online ISBN: 978-1-4684-9256-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation