scispace - formally typeset
Search or ask a question
Topic

Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


Papers
More filters
Proceedings Article
06 Jul 2015
TL;DR: Stochastic optimization, including prox-SMD and prox-SDCA, is studied with importance sampling, which improves the convergence rate by reducing the stochastic variance, and theoretically analyze and empirically validate their effectiveness.
Abstract: Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Mirror Descent (prox-SMD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estimate of the corresponding true quantity, the resulting estimator may have a rather high variance, which negatively affects the convergence of the underlying optimization procedure. In this paper we study stochastic optimization, including prox-SMD and prox-SDCA, with importance sampling, which improves the convergence rate by reducing the stochastic variance. We theoretically analyze the algorithms and empirically validate their effectiveness.

313 citations

Journal ArticleDOI
TL;DR: A comprehensive summary of the recent work on the theoretical development and practical applications of stochastic optimization approaches is presented.

313 citations

Journal ArticleDOI
TL;DR: A new demand side management technique, namely, a new energy efficient scheduling algorithm, is proposed to arrange the household appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model.
Abstract: High quality demand side management has become indispensable in the smart grid infrastructure for enhanced energy reduction and system control. In this paper, a new demand side management technique, namely, a new energy efficient scheduling algorithm, is proposed to arrange the household appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. The proposed algorithm takes into account the uncertainties in household appliance operation time and intermittent renewable generation. Moreover, it considers the variable frequency drive and capacity-limited energy storage. Our technique first uses the linear programming to efficiently compute a deterministic scheduling solution without considering uncertainties. To handle the uncertainties in household appliance operation time and energy consumption, a stochastic scheduling technique, which involves an energy consumption adaptation variable , is used to model the stochastic energy consumption patterns for various household appliances. To handle the intermittent behavior of the energy generated from the renewable resources, the offline static operation schedule is adapted to the runtime dynamic scheduling considering variations in renewable energy. The simulation results demonstrate the effectiveness of our approach. Compared to a traditional scheduling scheme which models typical household appliance operations in the traditional home scenario, the proposed deterministic linear programming based scheduling scheme achieves up to 45% monetary expense reduction, and the proposed stochastic design scheme achieves up to 41% monetary expense reduction. Compared to a worst case design where an appliance is assumed to consume the maximum amount of energy, the proposed stochastic design which considers the stochastic energy consumption patterns achieves up to 24% monetary expense reduction without violating the target trip rate of 0.5%. Furthermore, the proposed energy consumption scheduling algorithm can always generate the scheduling solution within 10 seconds, which is fast enough for household appliance applications.

312 citations

Journal ArticleDOI
Thomas Alerstam1
TL;DR: Using optimality perspectives is now regarded as an essential way of analysing and understanding adaptations and behavioural strategies in bird migration using stochastisch-dynamischer Programmierung, Jahresroutinemodellen and multiobjektiver Optimierung.
Abstract: Using optimality perspectives is now regarded as an essential way of analysing and understanding adaptations and behavioural strategies in bird migration Optimization analyses in bird migration research have diversified greatly during the two recent decades with respect to methods used as well as to topics addressed Methods range from simple analytical and geometric models to more complex modeling by stochastic dynamic programming, annual routine models and multiobjective optimization Also, game theory and simulation by selection algorithms have been used A wide range of aspects of bird migration have been analyzed including flight, fuel deposition, predation risk, stopover site use, transition to breeding, routes and detours, daily timing, fly-and-forage migration, wind selectivity and wind drift, phenotypic flexibility, arrival time and annual molt and migration schedules Optimization analyses have proven to be particularly important for defining problems and specifying questions and predictions about the consequences of minimization of energy, time and predation risk in bird migration Optimization analyses will probably also be important in the future, when predictions about bird migration strategies can be tested by much new data obtained by modern tracking techniques and when the importance of new trade-offs, associated with, eg, digestive physiology, metabolism, immunocompetence and disease, need to be assessed in bird migration research

312 citations

Journal ArticleDOI
TL;DR: In this article, the authors considered a virtual power plant consisting of an intermittent source, a storage facility, and a dispatchable power plant, and casted the offering problem as a two-stage stochastic mixed-integer linear programming model.

310 citations


Network Information
Related Topics (5)
Optimization problem
96.4K papers, 2.1M citations
86% related
Scheduling (computing)
78.6K papers, 1.3M citations
85% related
Optimal control
68K papers, 1.2M citations
84% related
Supply chain
84.1K papers, 1.7M citations
83% related
Markov chain
51.9K papers, 1.3M citations
79% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532