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Media type:
E-Article
Title:
Scalable Heuristics for a Class of Chance-Constrained Stochastic Programs
Contributor:
Watson, Jean-Paul;
Wets, Roger J-B;
Woodruff, David L.
Published:
Institute for Operations Research and the Management Sciences (INFORMS), 2010
Published in:
INFORMS Journal on Computing, 22 (2010) 4, Seite 543-554
Language:
English
DOI:
10.1287/ijoc.1090.0372
ISSN:
1091-9856;
1526-5528
Origination:
Footnote:
Description:
<jats:p> We describe computational procedures for solving a wide-ranging class of stochastic programs with chance constraints where the random components of the problem are discretely distributed. Our procedures are based on a combination of Lagrangian relaxation and scenario decomposition, which we solve using a novel variant of Rockafellar and Wets' progressive hedging algorithm [Rockafellar, R. T., R. J.-B. Wets. 1991. Scenarios and policy aggregation in optimization under uncertainty. Math. Oper. Res. 16(1) 119–147]. Experiments demonstrate the ability of the proposed algorithm to quickly find near-optimal solutions—where verifiable—to both difficult and very large chance-constrained stochastic programs, both with and without integer decision variables. The algorithm exhibits strong scalability in terms of both run time required and final solution quality on large-scale instances. </jats:p><jats:p> There is a Video Overview associated with this paper. Click here to view the Video Overview . To save the file, right click and choose “Save Link As” from the menu. </jats:p>