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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: In this paper, the authors used dynamic programming to derive optimal harvest strategies for mallards (Anas platyrhynchos) in which they balanced the competing objectives of maximizing long-term cumulative harvest and achieving a specified population goal.
Abstract: Those charged with regulating waterfowl harvests must cope with random environmental variations, incomplete control over harvest rates, and uncertainty about biological mechanisms operative in the population. Stochastic dynamic programming can be used effectively to account for these uncertainties if the probabilities associated with uncertain outcomes can be estimated. To use this approach managers must have clearly-stated objectives, a set of regulatory options, and a mathematical description of the managed system. We used dynamic programming to derive optimal harvest strategies for mallards (Anas platyrhynchos) in which we balanced the competing objectives of maximizing long-term cumulative harvest and achieving a specified population goal. Model-specific harvest strategies, which account for random variation in wetland conditions on the breeding grounds and for uncertainty about the relation between hunting regulations and harvest rates, are provided and compared. We also account for uncertainty in population dynamics with model probabilities, which express the relative confidence that alternative models adequately describe population responses to harvest and environmental conditions. Finally, we demonstrate how the harvest strategy thus derived can "evolve" as model probabilities are updated periodically using comparisons of model predictions and estimates of population size. J. WILDL. MANAGE. 61(1):202-216

164 citations

Journal ArticleDOI
TL;DR: In this paper, a methodology for financial risk management in the framework of two-stage stochastic programming for planning under uncertainty is presented, where a known probabilistic definition of financial risk is adapted to be used in this framework and its relation to downside risk is analyzed.
Abstract: A methodology is presented to include financial risk management in the framework of two-stage stochastic programming for planning under uncertainty. A known probabilistic definition of financial risk is adapted to be used in this framework and its relation to downside risk is analyzed. Using these definitions, new two-stage stochastic programming models that manage financial risk are presented. Computational issues related to these models are also discussed. © 2004 American Institute of Chemical Engineers AIChE J, 50: 963–989, 2004

163 citations

Journal ArticleDOI
TL;DR: This article provides a brief review of approximate dynamic programming, and how it should be approached from the perspective of different problem classes to make better decisions over time.
Abstract: Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well-known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 56: 239-249, 2009

163 citations

Book ChapterDOI
TL;DR: This chapter describes a stochastic ship routing problem with inventory management that involves finding a set of least cost routes for a fleet of ships transporting a single commodity when the demand for the commodity is uncertain.
Abstract: This chapter describes a stochastic ship routing problem with inventory management. The problem involves finding a set of least cost routes for a fleet of ships transporting a single commodity when the demand for the commodity is uncertain. Storage at supply and consumption ports is limited and inventory levels are monitored in the model. Consumer demands are at a constant rate within each time period, and in the stochastic problem, the demand rate for a period is not known until the beginning of that period. The demand situation over the time periods is described by a scenario tree with corresponding probabilities. A decomposition formulation is given and it is solved using a Branch and Price framework. A master problem (set partitioning with extra inventory constraints) is built, and the subproblems, one for each ship, are solved by stochastic dynamic programming and yield the columns for the master problem. Each column corresponds to one possible tree of actions for one ship giving its schedule loading/unloading quantities for all demand scenarios. Computational results are given showing that medium sized problems can be solved successfully.

163 citations

Book
01 Jan 1997
TL;DR: This paper presents a meta-anatomy of the optimization of nonstationary functions in the context of discrete-time decision-making using a reinforcement learning approach.
Abstract: Stochastic optimization.- On learning automata.- Unconstrained optimization problems.- Constrained optimization problems.- Optimization of nonstationary functions.

163 citations


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