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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: A stochastic mixed-integer linear programming model that involves both hydropower production and physical trading aspects is developed and the effects of including uncertainty explicitly into optimization by comparing the Stochastic approach to a deterministic approach are explored.

197 citations

Journal ArticleDOI
TL;DR: In this paper, the optimal harvest of a renewable resource in a generalized stochastic spatially explicit model is characterized, and a modified golden rule of growth that is independent of dispersal patterns is found.

197 citations

Journal ArticleDOI
TL;DR: A stochastic dynamic programming approach, linked to a metapopulation model, was used to find optimal release strategies, given constraints on time and the number of biocontrol agents available, and derived rules of thumb that will enable biOControl workers to choose between management options, depending on the current state of the system.
Abstract: 1. Establishing biological control agents in the field is a major step in any classical biocontrol programme, yet there are few general guidelines to help the practitioner decide what factors might enhance the establishment of such agents. 2. A stochastic dynamic programming (SDP) approach, linked to a metapopulation model, was used to find optimal release strategies (number and size of releases), given constraints on time and the number of biocontrol agents available. By modelling within a decision-making framework we derived rules of thumb that will enable biocontrol workers to choose between management options, depending on the current state of the system. 3. When there are few well-established sites, making a few large releases is the optimal strategy. For other states of the system, the optimal strategy ranges from a few large releases, through a mixed strategy (a variety of release sizes), to many small releases, as the probability of establishment of smaller inocula increases. 4. Given that the probability of establishment is rarely a known entity, we also strongly recommend a mixed strategy in the early stages of a release programme, to accelerate learning and improve the chances of finding the optimal approach.

197 citations

Journal ArticleDOI
TL;DR: In this article, an adjustable robust restoration optimization model with a two-stage objective is proposed, involving the uncertain DG outputs and load demands, where the first stage generates optimal strategies for recovery of outage power and the second stage seeks the worst-case fluctuation scenarios.
Abstract: Distributed generations (DGs) introduce significant uncertainties to restoration of active distribution networks, in addition to roughly estimated load demands. An adjustable robust restoration optimization model with a two-stage objective is proposed in this paper, involving the uncertain DG outputs and load demands. The first stage generates optimal strategies for recovery of outage power and the second stage seeks the worst-case fluctuation scenarios. The model is formulated as a mixed-integer linear programming problem and solved using the column-and-constraint generation method. The feasibility and reliability of the strategies obtained via this robust optimization model can be guaranteed for all cases in the predefined uncertainty sets with good performance. A technique known as the uncertainty budget is used to adjust the conservativeness of this model, providing a tradeoff between conservativeness and robustness. Numerical tests are carried out on the modified PG&E 69-bus system and a modified 246-bus system to compare the robust optimization model against a deterministic restoration model, which verifies the superiority of this proposed model.

196 citations

Journal ArticleDOI
TL;DR: To solve the complicated nonlinear, non-smooth, and non-differentiable SDEED, an enhanced particle swarm optimization (PSO) algorithm is applied to obtain the best solution for the corresponding scenarios to improve the quality of the solutions attained by PSO.

196 citations


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