Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
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TL;DR: A scenario-based approach for capturing the evolution of demand and a stochastic programming model for determining technology choices and capacity plans are developed, which is likely to be large and may not be easy to solve with standard software packages.
122 citations
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15 Mar 2009TL;DR: In this article, the benefits of a combined approach that uses stochastic and reserve methods for the efficient management of uncertainty in the unit commitment problem for systems with significant amount of wind power were evaluated.
Abstract: Day-ahead uncertainty management in power systems has traditionally been approached by means of multistage decision making and operating reserve requirements An alternate approach for managing uncertainty is a stochastic formulation, which allows the explicit modeling of the sources of uncertainty The large investments in wind power has increased the importance of operations uncertainty management due to the considerable operational uncertainty wind plants have This paper evaluates the benefits of a combined approach that uses stochastic and reserve methods for the efficient management of uncertainty in the unit commitment problem for systems with significant amount of wind power Numerical studies on a model of the PSCo system show that the unit commitment solutions obtained for the combined approach are robust and superior with respect to the traditional approach in terms of economic metrics and curtailed wind power
122 citations
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TL;DR: In this paper, a unified approach to various problems of structural optimization is presented, based on a combination of mathematical models of different complexity, which describe the behaviour of a designed structure and are connected with the sequential approximation of design problem constraints and/or an objective function.
Abstract: A unified approach to various problems of structural optimization is presented. It is based on a combination of mathematical models of different complexity. The models describe the behaviour of a designed structure. From the computational point of view, it is connected with the sequential approximation of design problem constraints and/or an objective function. In each step, a subregion of the initial search region in the space of design variables is chosen. In this subregion, various points (designs) are selected, for which response analyses are carried out using a numerical method (mostly FEM). Using the least-squares method, analytical expressions are formulated, which then replace the initial problem functions. They are used as functions of a particular mathematical programming problem. The size and location of sequential subregions may be changed according to the result of the search. The choice of one particular form of the analytical expressions is described. The application of the approach is shown by means of test examples and comparison with other optimization techniques is presented.
122 citations
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01 Jan 2001
TL;DR: A Finite-Dimensional Approach to Infinite-Ddimensional Constraints in Stochastic Programming Duality and Hierarchical Sparsity in Multistage Convex Stochastics Programs M. Steinbach.
Abstract: Preface. Output analysis for approximated stochastic programs J. Dupacova. Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments P. Efraimidis, P.G. Spirakis. Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Stochastic Programming Analysis A. Gaivoronski, et al. Option pricing in a world with arbitrage X. Guo, L. Shepp. Monte Carlo Methods for Discrete Stochastic Optimization T. Homem-de-Mello. Discrete Approximation in Quantile Problem of Portfolio Selection A. Kibzun, R. Lepp. Optimizing electricity distribution using two-stage integer recourse models W.K. Klein Haneveld, M.H. van der Vlerk. A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Duality L. Korf. Non-Linear Risk of Linear Instruments A. Kreinin. Multialgorithms for Parallel Computing: A New Paradigm for Optimization J. Nazareth. Convergence Rate of Incremental Subgradient Algorithms A. Nedic, D. Bertsekas. Transient Stochastic Models for Search Patterns E. Pasiliao. Value-at-Risk Based Portfolio Optimization A. Puelz. Combinatorial Optimization, Cross-Entropy, Ants and Rare Events R.Y. Rubinstein. Consistency of Statistical Estimators: the Epigraphical View G. Salinetti. Hierarchical Sparsity in Multistage Convex Stochastic Programs M. Steinbach. Conditional Value-at-Risk: Optimization Approach S. Uryasev, R.T. Rockafellar.
122 citations
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TL;DR: This paper provides a theoretical framework ofdependent-chance programming, as well as dependent-chance multiobjective programming and dependent-Chance goal programming which are new types of stochastic optimization.
Abstract: This paper provides a theoretical framework of dependent-chance programming, as well as dependent-chance multiobjective programming and dependent-chance goal programming which are new types of stochastic optimization A stochastic simulation based genetic algorithm is also designed for solving dependent-chance programming models
122 citations