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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: This paper develops and analyzes a model of an oil property, where production rates and oil prices both vary stochastically over time and the decision maker may terminate production or accelerate production by drilling additional wells.
Abstract: There are two major competing procedures for evaluating risky projects where managerial flexibility plays an important role: one is decision analytic, based on stochastic dynamic programming, and the other is option pricing theory (or contingent claims analysis), based on the no-arbitrage theory of financial markets. In this paper, we show how these two approaches can be profitably integrated to evaluate oil properties. We develop and analyze a model of an oil property-either a developed property or a proven but undeveloped reserve-where production rates and oil prices both vary stochastically over time and, at any time, the decision maker may terminate production or accelerate production by drilling additional wells. The decision maker is assumed to be risk averse and can hedge price risks by trading oil futures contracts. We also describe extensions of this model that incorporate additional uncertainties and options, discuss its use in exploration decisions and in evaluating a portfolio of properties rather than a single property, and briefly describe other potential applications of this integrated methodology.

243 citations

Journal ArticleDOI
TL;DR: Heuristics, based on the properties of the optimal solutions, are developed to find "good" solutions for the general problem and derive upper bounds which are useful when evaluating the performance of the heuristics.
Abstract: In this paper we study optimal strategies for renting hotel rooms when there is a stochastic and dynamic arrival of customers from different market segments. We formulate the problem as a stochastic and dynamic programming model and characterize the optimal policies as functions of the capacity and the time left until the end of the planning horizon. We consider three features that enrich the problem: we make no assumptions concerning the particular order between the arrivals of different classes of customers; we allow for multiple types of rooms and downgrading; and we consider requests for multiple nights. We also consider implementations of the optimal policy. The properties we derive for the optimal solution significantly reduce the computational effort needed to solve the problem, yet in the multiple product and/or multiple night case this is often not enough. Therefore, heuristics, based on the properties of the optimal solutions, are developed to find "good" solutions for the general problem. We also derive upper bounds which are useful when evaluating the performance of the heuristics. Computational experiments show a satisfactory performance of the heuristics in a variety of scenarios using real data from a medium size hotel.

242 citations

Journal ArticleDOI
TL;DR: This paper presents a two-stage stochastic programming approach to the optimal scheduling of a resilient MG, linearized which offers robustness, simplicity, and computational efficiency in optimizing the MG operation.
Abstract: In recent years, natural disasters around the world have underscored the need for operative solutions that can improve the power grid resilience in response to low-probability high-impact incidents. The advent of microgrids (MGs) in modern power systems has introduced promising measures that can fulfil the power network resiliency requirements. This paper presents a two-stage stochastic programing approach to the optimal scheduling of a resilient MG. The impact of natural disasters on the optimal operation of MGs is modeled using a stochastic programming process. Other prevailing uncertainties associated with wind energy, electric vehicles, and real-time market prices are also taken into account. The proposed hourly scheme attempts to mitigate damaging impacts of electricity interruptions by effectively exploiting the MG capabilities. Incorporating AC network constraints in the proposed model offers a better solution to the security-constrained operation of MGs. The proposed model is linearized which offers robustness, simplicity, and computational efficiency in optimizing the MG operation. The effectiveness of proposed approach is illustrated using a large-scale MG test bed with a realistic set of data.

240 citations

Journal ArticleDOI
TL;DR: A set of percentile criteria that are conceptually natural and representative of the trade-off between optimistic and pessimistic views of the question are presented and the use of these criteria under different forms of uncertainty for both the rewards and the transitions is studied.
Abstract: Markov decision processes are an effective tool in modeling decision making in uncertain dynamic environments. Because the parameters of these models typically are estimated from data or learned from experience, it is not surprising that the actual performance of a chosen strategy often differs significantly from the designer's initial expectations due to unavoidable modeling ambiguity. In this paper, we present a set of percentile criteria that are conceptually natural and representative of the trade-off between optimistic and pessimistic views of the question. We study the use of these criteria under different forms of uncertainty for both the rewards and the transitions. Some forms are shown to be efficiently solvable and others highly intractable. In each case, we outline solution concepts that take parametric uncertainty into account in the process of decision making.

239 citations

Journal ArticleDOI
TL;DR: A stochastic dynamic programming framework for the optimal energy management of a smart home with plug-in electric vehicle (PEV) energy storage is proposed, to minimize electricity ratepayer cost, while satisfying home power demand and PEV charging requirements.
Abstract: This paper proposes a stochastic dynamic programming framework for the optimal energy management of a smart home with plug-in electric vehicle (PEV) energy storage. This paper is motivated by the challenges associated with intermittent renewable energy supplies and the local energy storage opportunity presented by vehicle electrification. This paper seeks to minimize electricity ratepayer cost, while satisfying home power demand and PEV charging requirements. First, various operating modes are defined, including vehicle-to-grid, vehicle-to-home, and grid-to-vehicle. Second, we use equivalent circuit PEV battery models and probabilistic models of trip time and trip length to formulate the PEV to smart home energy management stochastic optimization problem. Finally, based on time-varying electricity price and time-varying home power demand, we examine the performance of the three operating modes for typical weekdays.

239 citations


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