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: The test showed that dual dynamic programming has reasonable computing times and can be a useful tool in stochastic scheduling in a hydro-dominated system.
Abstract: The aim is to show an application of stochastic dual dynamic programming to seasonal planning in a part of the Norwegian hydro-dominated power system. The subsystem under study has 35 reservoirs on 28 watercourses. It is found that for the study system the new procedure is entirely feasible and gives good results. Two implementation details are studied more closely: use of relaxation in the solution of the subproblems, and a starting technique, called pre-segment, to save iterations in the overall problem. Both are found to have a significant effect on computer time. The test showed that dual dynamic programming has reasonable computing times and can be a useful tool in stochastic scheduling in a hydro-dominated system. >
94 citations
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TL;DR: In this article, the authors discuss statistical inference of sample average approximations of multistage stochastic programming problems and show that any random sampling scheme provides a valid statistical lower bound for the optimal (minimum) value of the true problem.
Abstract: We discuss in this paper statistical inference of sample average approximations of multistage stochastic programming problems. We show that any random sampling scheme provides a valid statistical lower bound for the optimal (minimum) value of the true problem. However, in order for such lower bound to be consistent one needs to employ the conditional sampling procedure. We also indicate that fixing a feasible first-stage solution and then solving the sampling approximation of the corresponding (T−1)-stage problem, does not give a valid statistical upper bound for the optimal value of the true problem.
94 citations
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TL;DR: By using this alternative and simple approach, the computing time for solving transmission expansion planning problems has been reduced drastically and the issue of improving computational performance by taking different features from existing algorithms is addressed.
94 citations
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TL;DR: This paper addresses the problem of robustness of multiuser MIMO receivers against imperfect CSI and proposes a new linear technique that guarantees the robustness against CSI errors with a certain selected probability.
Abstract: Traditional multiuser receiver algorithms developed for multiple-input-multiple-output (MIMO) wireless systems are based on the assumption that the channel state information (CSI) is precisely known at the receiver. However, in practical situations, the exact CSI may be unavailable because of channel estimation errors and/or outdated training. In this paper, we address the problem of robustness of multiuser MIMO receivers against imperfect CSI and propose a new linear technique that guarantees the robustness against CSI errors with a certain selected probability. The proposed receivers are formulated as probabilistically constrained stochastic optimization problems. Provided that the CSI mismatch is Gaussian, each of these problems is shown to be convex and to have a unique solution. The fact that the CSI mismatch is Gaussian also enables to convert the original stochastic problems to a more tractable deterministic form and to solve them using the second-order cone programming approach. Numerical simulations illustrate an improved robustness of the proposed receivers against CSI errors and validate their better flexibility as compared with the robust multiuser MIMO receivers based on the worst case designs
94 citations
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TL;DR: In this paper, a model to assist decision makers in the logistics of a flood emergency is presented, which attempts to optimize inventory levels for emergency supplies as well as vehicles availability, in order to deliver enough supplies to satisfy demands with a given probability.
Abstract: This article presents a model to assist decision makers in the logistics of a flood emergency. The model attempts to optimize inventory levels for emergency supplies as well as vehicles’ availability, in order to deliver enough supplies to satisfy demands with a given probability. A spatio-temporal stochastic process represents the flood occurrence. The model is approximately solved with sample average approximation. The article presents a method to quantify the impact of the various intervening logistics parameters. An example is provided and a sensitivity analysis is performed. The studied example shows large differences between the impacts of logistics parameters such as number of products, number of periods, inventory capacity and degree of demand fulfillment on the logistics cost and time. This methodology emerges as a valuable tool to help decision makers to allocate resources both before and after a flood occurs, with the aim of minimizing the undesirable effects of such events.
94 citations