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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: The downside risk constraints (DRC) are proposed to minimize the risk associated with uncertainties in order to obtain the risk-based scheduling of energy hub via scenario-based stochastic programming and the effect of the DRP on the problem is investigated and the results show that the expected cost and RIC are decreased.

109 citations

Book
09 Oct 2006
TL;DR: This book provides a practical introduction to computationally solving discrete optimization problems using dynamic programming from the examples presented, so that readers should more easily be able to formulate dynamic programming solutions to their own problems of interest.
Abstract: This book provides a practical introduction to computationally solving discrete optimization problems using dynamic programming. From the examples presented, readers should more easily be able to formulate dynamic programming solutions to their own problems of interest. We also provide and describe the design, implementation, and use of a software tool that has been used to numerically solve all of the problems presented earlier in the book.

109 citations

Journal ArticleDOI
TL;DR: In this article, a stochastic pooling problem optimization formulation is proposed to address the product quality and uncertainty in natural gas production networks, where the qualities of the flows in the system are described with a pooling model and the uncertainty of the system is handled with a multiscenario, two-stage Stochastic recourse approach in addition, multi-objective problems are handled via a hierarchical optimization approach.
Abstract: Product quality and uncertainty are two important issues in the design and operation of natural gas production networks This paper presents a stochastic pooling problem optimization formulation to address these two issues, where the qualities of the flows in the system are described with a pooling model and the uncertainty in the system is handled with a multiscenario, two-stage stochastic recourse approach In addition, multi-objective problems are handled via a hierarchical optimization approach The advantages of the proposed formulation are demonstrated with case studies involving an example system based on Haverly’s pooling problem and a real industrial system The stochastic pooling problem is a potentially large-scale nonconvex Mixed-Integer Nonlinear Program (MINLP), and a rigorous decomposition method developed recently is used to solve this problem A computational study demonstrates the advantage of the decomposition method over a state-of-the-art branchand-reduce global optimizer, BARON VC 2010 American Institute of Chemical Engineers AIChE J, 00: 000–000, 2010

109 citations

Journal ArticleDOI
TL;DR: This paper investigates the computation of optimal policies in constrained discrete stochastic dynamic programming with the average reward as utility function, and an algorithm to compute such an optimal policy is presented.
Abstract: In this paper we investigate the computation of optimal policies in constrained discrete stochastic dynamic programming with the average reward as utility function. The state-space and action-sets are assumed to be finite. Constraints which are linear functions of the state-action frequencies are allowed. In the general multichain case, an optimal policy will be a randomized nonstationary policy. An algorithm to compute such an optimal policy is presented. Furthermore, sufficient conditions for optimal policies to be stationary are derived. There are many applications for constrained undiscounted stochastic dynamic programming, e.g., in multiple objective Markovian decision models.

109 citations

Journal ArticleDOI
TL;DR: The solution of the two-stage fixed recourse problem is considered, for which a sensitivity-based successive disaggregation algorithm is proposed, and several example problems are solved where the certainty equivalent problem involves millions of variables and constraints.

109 citations


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