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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.


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Journal ArticleDOI
TL;DR: This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EModPS solution design as well as of the resulting Pareto-approximate operating policies.
Abstract: Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP’s practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa ...

177 citations

Journal ArticleDOI
TL;DR: The minimax approach to stochastic programming with recourse deals with the case of incomplete knowledge of the distribution F of the random coefficients and leads to the deterministic program [ILM0001] where x is a given set of admissible solutions, φ is a recourse function and f is aGiven set of distributions.
Abstract: The minimax approach to stochastic programming with recourse deals with the case of incomplete knowledge of the distribution F of the random coefficients. It leads to the deterministic program [ILM0001] where x is a given set of admissible solutions, φis a recourse function and f is a given set of distributions. To express the objective function of (*) in a form suitable for further computation, general results on the moment problem can be used.In section 1, the main ideas are briefly surveyed. Subsequently, the method is applied to (*) in section 2. It is shown how the results can be used both to draw conclusions on robustness of the optimal value of the given stochastic program with respect to the set of distributions considered, and to study sensitivity of the optimal solution with respect to a specified distribution. In section 3, an application to a stochastic model of a water resource system with incomplete knowledge about the distribution of the random demand is suggested

177 citations

Journal ArticleDOI
TL;DR: It is verified that problems with fixed recourse are characterized by scenario-dependent second stage convexifications that have a great deal in common, and a decomposition-based algorithm is developed which is referred to as Disjunctive Decomposition (D2).
Abstract: This paper considers the two-stage stochastic integer programming problem, with an emphasis on instances in which integer variables appear in the second stage. Drawing heavily on the theory of disjunctive programming, we characterize convexifications of the second stage problem and develop a decomposition-based algorithm for the solution of such problems. In particular, we verify that problems with fixed recourse are characterized by scenario-dependent second stage convexifications that have a great deal in common. We refer to this characterization as the C3 (Common Cut Coefficients) Theorem. Based on the C3 Theorem, we develop a decomposition algorithm which we refer to as Disjunctive Decomposition (D2). In this new class of algorithms, we work with master and subproblems that result from convexifications of two coupled disjunctive programs. We show that when the second stage consists of 0-1 MILP problems, we can obtain accurate second stage objective function estimates after finitely many steps. This result implies the convergence of the D2 algorithm.

177 citations

Book ChapterDOI
25 Sep 2006
TL;DR: It is demonstrated for the first time how information about an algorithm's parameter settings can be incorporated into a model, and how such models can be used to automatically adjust the algorithm's parameters on a per-instance basis in order to optimize its performance.
Abstract: Machine learning can be used to build models that predict the run-time of search algorithms for hard combinatorial problems. Such empirical hardness models have previously been studied for complete, deterministic search algorithms. In this work, we demonstrate that such models can also make surprisingly accurate predictions of the run-time distributions of incomplete and randomized search methods, such as stochastic local search algorithms. We also show for the first time how information about an algorithm's parameter settings can be incorporated into a model, and how such models can be used to automatically adjust the algorithm's parameters on a per-instance basis in order to optimize its performance. Empirical results for Novelty+ and SAPS on structured and unstructured SAT instances show very good predictive performance and significant speedups of our automatically determined parameter settings when compared to the default and best fixed distribution-specific parameter settings.

176 citations

Journal ArticleDOI
TL;DR: Technical aspects of the Russell-Yasuda Kasai financial planning model are discussed, including the models for the discrete distribution scenario generation processes for the uncertain parameters of the model, and a comparison of algorithms used in the model's solution.
Abstract: This paper discusses technical aspects of the Russell-Yasuda Kasai financial planning model. These include the models for the discrete distribution scenario generation processes for the uncertain parameters of the model, the mathematical approach used to develop the infinite-horizon end-effects part of the model, a comparison of algorithms used in the model's solution, and a comparison of the multistage stochastic linear programming model with the previous technology, static mean-variance analysis. Experience and benefits of the model in Yasuda-Kasai's financial planning process is also discussed.

176 citations


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