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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 article, a mixed integer stochastic programming model is established to support strategic planning of bioenergy supply chain systems and optimal feedstock resource allocation in an uncertain decision environment, together with a Lagrange relaxation based decomposition solution algorithm, was implemented in a real-world case study in California to explore the potential of waste-based bioethanol production.
Abstract: A mixed integer stochastic programming model is established to support strategic planning of bioenergy supply chain systems and optimal feedstock resource allocation in an uncertain decision environment. The two-stage stochastic programming model, together with a Lagrange relaxation based decomposition solution algorithm, was implemented in a real-world case study in California to explore the potential of waste-based bioethanol production. The model results show that biowaste-based ethanol can be a viable part of sustainable energy solution for the future.

219 citations

Book
29 Feb 1996
TL;DR: The aim of this monograph is to provide a scaffolding for future studies of Stochastic Decomposition, as well as some guidelines for computer implementation, to aid in the development of such a system.
Abstract: Preface. 1. Two Stage Stochastic Linear Programs. 2. Sampling Within Stochastic Linear Programming. 3. Foundations of Stochastic Decomposition. 4. Stabilizing Stochastic Decomposition. 5. Stopping Rules for Stochastic Decomposition. 6. Guidelines for Computer Implementation. 7. Illustrative Computational Experiments. Glossary.

219 citations

Journal ArticleDOI
TL;DR: It is shown that adding new source of heat energy for providing demand of consumers with market mechanism changes the optimal operation point of multi carrier energy system.

218 citations

Book ChapterDOI
01 Jan 2010
TL;DR: In this article, a two-stage hybrid search method, called Eagle Strategy, was proposed for stochastic optimization problems, which combines the random search using Levy walk with the firefly algorithm in an iterative manner.
Abstract: Most global optimization problems are nonlinear and thus difficult to solve, and they become even more challenging when uncertainties are present in objective functions and constraints. This paper provides a new two-stage hybrid search method, called Eagle Strategy, for stochastic optimization. This strategy intends to combine the random search using Levy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochastic optimization. Finally practical implications and potential topics for further research will be discussed.

217 citations

Journal ArticleDOI
TL;DR: The Concave, Adaptive Value Estimation (CAVE) as discussed by the authors algorithm constructs a sequence of concave piecewise linear approximations using sample gradients of the recourse function at different points in the domain.
Abstract: We consider the problem of optimizing inventories for problems where the demand distribution is unknown, and where it does not necessarily follow a standard form such as the normal. We address problems where the process of deciding the inventory, and then realizing the demand, occurs repeatedly. The only information we use is the amount of inventory left over. Rather than attempting to estimate the demand distribution, we directly estimate the value function using a technique called the Concave, Adaptive Value Estimation CAVE algorithm. CAVE constructs a sequence of concave piecewise linear approximations using sample gradients of the recourse function at different points in the domain. Since it is a sampling-based method, CAVE does not require knowledge of the underlying sample distribution. The result is a nonlinear approximation that is more responsive than traditional linear stochastic quasi-gradient methods and more flexible than analytical techniques that require distribution information. In addition, we demonstrate near-optimal behavior of the CAVE approximation in experiments involving two different types of stochastic programs-the newsvendor stochastic inventory problem and two-stage distribution problems.

217 citations


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