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
AMS(MOS) subject classifications
Research supported in part by a grant of the National Science Foundation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
T. Helgason and S.W. Wallace. Approximate scenario solution in the progressive hedging algorithm. RH-08–89, Raumvísindastofnun Háskólans, 1989.
J.M. Mulvey and A. Ruszczynski. A new scenario decomposition method for large-scale stochastic optimization. Operations Research, 43:477–490, 1995.
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.
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.
R.T. Rockafellar and R.J-B Wets. Scenarios and policy aggregation in optimization under uncertainty. Mathematics of Operations Research, 16:119–147, 1991.
R.T. Rockafellar and R.J-B Wets. Variational Analysis. Springer, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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