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Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a branch-and-cut procedure for stochastic integer programs with complete recourse and first stage binary variables is presented, which is shown to provide a finite exact algorithm for a number of integer programs, even in the presence of binary variables or continuous random variables in the second stage.

598 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed a model and a solution technique for the problem of generating electric power when demands are not certain, and provided techniques for improving the current methods used in solving the traditional unit commitment problem.
Abstract: The authors develop a model and a solution technique for the problem of generating electric power when demands are not certain. They also provide techniques for improving the current methods used in solving the traditional unit commitment problem. The solution strategy can be run in parallel due to the separable nature of the relaxation used. Numerical results indicate significant savings in the cost of operating power generating systems when the stochastic model is used instead of the deterministic model.

593 citations

Journal ArticleDOI
TL;DR: A two-stage stochastic programming model for committing reserves in systems with large amounts of wind power outperforms common reserve rules and is tested on a model of California consisting of 122 generators.
Abstract: We present a two-stage stochastic programming model for committing reserves in systems with large amounts of wind power. We describe wind power generation in terms of a representative set of appropriately weighted scenarios, and we present a dual decomposition algorithm for solving the resulting stochastic program. We test our scenario generation methodology on a model of California consisting of 122 generators, and we show that the stochastic programming unit commitment policy outperforms common reserve rules.

587 citations

BookDOI
01 Mar 1988
TL;DR: This is a comprehensive and timely overview of the numerical techniques that have been developed to solve stochastic programming problems and comprehensively covers all major advances in the field (both Western and Soviet).
Abstract: This is a comprehensive and timely overview of the numerical techniques that have been developed to solve stochastic programming problems. After a brief introduction to the field, where accent is laid on modeling questions, the next few chapters lay out the challenges that must be met in this area. They also provide the background for the description of the computer implementations given in the third part of the book. Selected applications are described next. Some of these have directly motivated the development of the methods described in the earlier chapters. They include problems that come from facilities location, exploration investments, control of ecological systems, energy distribution and generation. Test problems are collected in the last chapter. This is the first book devoted to this subject. It comprehensively covers all major advances in the field (both Western and Soviet). It is only because of the recent developments in computer technology, that we have now reached a point where our computing power matches the inherent size requirements faced in this area. The book demonstrates that a large class of stochastic programming problems are now in the range of our numerical capacities.

584 citations

Journal ArticleDOI

583 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532