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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: This paper provides an in-depth review of systematic techniques that address uncertainties for ESOMs: Monte Carlo analysis, stochastic programming, robust optimization, and modelling to generate alternatives to provide extra policy insights.

118 citations

Proceedings ArticleDOI
18 Jun 2012
TL;DR: This paper links incentive to users' demand for consuming compelling services, as an approach complementary to conventional credit or reputation based approaches, and designs two incentive schemes, Incentive with Demand Fairness (IDF) and Iterative Tank Filling (ITF), for maximizing fairness and social welfare, respectively.
Abstract: Participatory sensing has emerged recently as a promising approach to large-scale data collection. However, without incentives for users to regularly contribute good quality data, this method is unlikely to be viable in the long run. In this paper, we link incentive to users' demand for consuming compelling services, as an approach complementary to conventional credit or reputation based approaches. With this demand-based principle, we design two incentive schemes, Incentive with Demand Fairness (IDF) and Iterative Tank Filling (ITF), for maximizing fairness and social welfare, respectively. Our study shows that the IDF scheme is max-min fair and can score close to 1 on the Jain's fairness index, while the ITF scheme maximizes social welfare and achieves a unique Nash equilibrium which is also Pareto and globally optimal. We adopted a game theoretic approach to derive the optimal service demands. Furthermore, to address practical considerations, we use a stochastic programming technique to handle uncertainty that is often encountered in real life situations.

118 citations

Journal ArticleDOI
TL;DR: Rules are given that enable the transformation of a0-1 polynomial programming problem into a 0-1 linear programming problem to be effected with reduced numbers of constraints.
Abstract: This paper gives rules that enable the transformation of a 0-1 polynomial programming problem into a 0-1 linear programming problem to be effected with reduced numbers of constraints. Rules are also given that provide reduced numbers of variables when the true variables of interest are not individual cross-product terms, but sums of such terms or polynomials of the form ∑xjp.

118 citations

Journal ArticleDOI
TL;DR: In this paper, an efficient solution approach based on Benders' decomposition is proposed to solve a network-constrained ac unit commitment problem under uncertainty, which is modeled through a suitable set of scenarios.
Abstract: This paper proposes an efficient solution approach based on Benders’ decomposition to solve a network-constrained ac unit commitment problem under uncertainty. The wind power production is the only source of uncertainty considered in this paper, which is modeled through a suitable set of scenarios. The proposed model is formulated as a two-stage stochastic programming problem, whose first-stage refers to the day-ahead market, and whose second-stage represents real-time operation. The proposed Benders’ approach allows decomposing the original problem, which is mixed-integer nonlinear and generally intractable, into a mixed-integer linear master problem and a set of nonlinear, but continuous subproblems, one per scenario. In addition, to temporally decompose the proposed ac unit commitment problem, a heuristic technique is used to relax the inter-temporal ramping constraints of the generating units. Numerical results from a case study based on the IEEE one-area reliability test system (RTS) demonstrate the usefulness of the proposed approach.

118 citations

Journal ArticleDOI
TL;DR: In this paper, a method based on stochastic dynamic programming is proposed to handle uncertainties in important variables such as energy demand and prices of energy carriers together with the dynamics of the system.
Abstract: Most generation expansion planning tools do not model uncertainties in important variables such as energy demand and prices of energy carriers together with the dynamics of the system. A method for handling these uncertainties in generation expansion problems is described. The method is based on stochastic dynamic programming. As the uncertain variables are modeled by Markov chains they give a natural year-to-year dependence of the variables. This modeling makes it possible to describe the connection between investment decisions, time, construction periods, and uncertainty. The importance of modeling these connections is demonstrated by a realistic example. >

118 citations


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