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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Papers
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TL;DR: In this article, a stochastic programming model is used to represent the uncertain parameters plaguing such a long-term planning exercise, and the transition from today to 2050 is represented by allowing investment in both production and transmission facilities, with the target of achieving a renewable-dominated minimum-cost system.
Abstract: Renewable energy sources are here to stay for a number of important reasons, including global warming and the depletion of fossil fuels. We explore in this paper how a thermal-dominated electric energy system can be transformed into a renewable-dominated one. This study relies on a stochastic programming model that allows representing the uncertain parameters plaguing such long-term planning exercise. Being the final year of our analysis 2050, we represent the transition from today to 2050 by allowing investment in both production and transmission facilities, with the target of achieving a renewable-dominated minimum-cost system. The methodology developed is illustrated using a realistic large-scale case study. Finally, policy conclusions are drawn.
118 citations
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TL;DR: MDPtoolbox is presented, a multi-platform set of functions to solve Markov decision problems (MATLAB, GNU Octave, Scilab and R) and provides state-of-the-art and ready to use algorithms to solve a wide range of MDPs.
Abstract: Stochastic dynamic programming (SDP) or Markov decision processes (MDP) are increasingly being used in ecology to find the best decisions over time and under uncertainty so that the chance of achieving an objective is maximised. To date, few programs are available to solve SDP/MDP. We present MDPtoolbox, a multi-platform set of functions to solve Markov decision problems (MATLAB, GNU Octave, Scilab and R). MDPtoolbox provides state-of-the-art and ready to use algorithms to solve a wide range of MDPs. MDPtoolbox is easy to use, freely available and has been continuously improved since 2004. We illustrate how to use MDPtoolbox on a dynamic reserve design problem.
118 citations
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TL;DR: In this paper, a multi-objective mathematical programming model for use in the design of a sustainable supply chain network under uncertain conditions is presented, aimed at maximizing social benefits while minimizing economic costs and environmental impacts.
117 citations
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TL;DR: In this paper, a stochastic dynamic programming model was developed to obtain optimal acquisition and sale strategies for the U.S. oil reserve, and the model incorporates quota or tariff policies which may be used in conjunction with the stockpile policy.
Abstract: This article develops a stochastic dynamic programming model which may be used to obtain optimal acquisition and sale strategies for the U.S. oil reserve. The model incorporates quota or tariff policies which may be used in conjunction with the stockpile policy. Although the main focus is on U.S. stockpile policy, a joint consumer country policy is also considered. The analysis indicates the importance of the degree of oil supply response in determining the effectiveness of a stockpile policy.
117 citations
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TL;DR: This paper provides a framework ofdependent-chance programming as well as dependent-chance multiobjective programming and dependent-Chance goal programming in fuzzy environment as opposed to stochastic environment and extends the concepts of uncertain environments, events, chance functions and induced constraints from stochastics to fuzzy cases.
117 citations