Stochastic Integer Programming

When introducing integer variables into traditional linear stochastic programs structural properties and algorithmic approaches have to be rethought from the very beginning. Employing basics from parametric integer programming and probability theory we analyse the structure of stochastic integer programs. In the algorithmic part of the paper we review solution techniques from integer programming and discuss their impact on the specialized structures met in stochastic programming.

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Date Created:
10.02.2003
Date Issued:
10.07.2012
URN:
urn:nbn:de:hbz:464-20120710-095240-2
PURL:
http://purl.oclc.org/NET/duett-04142003-104231
DOI:
10.1016/S0927-0507(03)10004-7
Language:
English
Type of Resource:
Text
Keywords:
90C11 Mixed integer programming; cutting planes; stochastic integer programs; Lagrangian relaxation; multi-stage models; simple integer recourse; sampling methods; decomposition schemes; mixed-integer recourse; 90C15 Stochastic programming; integer L-shaped algorithm
Collection:
E-Publications
Dewey Decimal Classification:
510 Mathematics
Subject categories of the Deutsche Nationalbibliografie:
510 Mathematics
Hosting institution:
Faculty of Mathematics
Info on primary publication:

This is a Preprint version of this article:

Schultz, Rüdiger/Louveaux, Francois V.: Stochastic Integer Programming. In: Handbooks in Operations Research and Management Science, Vol. 10, 2003, pp. 213-266.

The final article version is available online at: https://doi.org/10.1016/S0927-0507(03)10004-7

 

version identification:
Preprint

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