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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TL;DR: A two-stage stochastic programming model for provision of flexible demand response (DR) based on thermal energy storage in the form of hot water storage and/or storage in building material and the utilization of the ETD metric to facilitate quantification of the expected total (energy and thermal discomfort) cost is demonstrated.
Abstract: This paper presents a two-stage stochastic programming model for provision of flexible demand response (DR) based on thermal energy storage in the form of hot water storage and/or storage in building material. Aggregated residential electro-thermal technologies (ETTs), such as electric heat pumps and (micro-) combined heat and power, are modeled in a unified nontechnology specific way. Day-ahead optimization is carried out considering uncertainty in outdoor temperature, electricity and hot water consumption, dwelling occupancy, and imbalance prices. Building flexibility is exploited through specification of a deadband around the set temperature or a price of thermal discomfort applied to deviations from the set temperature. A new expected thermal discomfort (ETD) metric is defined to quantify user discomfort. The efficacy of exploiting the flexibility of various residential ETT following the two approaches is analyzed. The utilization of the ETD metric to facilitate quantification of the expected total (energy and thermal discomfort) cost is also demonstrated. Such quantification may be useful in the determination of DR contracts set up by energy service companies. Case studies for a U.K. residential users’ aggregation exemplify the model proposed and quantify possible cost reductions that are achievable under different flexibility scenarios.
162 citations
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TL;DR: In this paper, a two-stage stochastic programming model is proposed for defining optimal periodic review policies for red blood cells inventory management that focus on minimising operational costs, as well as blood shortage and wastage due to outdating, taking into account perishability and demand uncertainty.
161 citations
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TL;DR: In this paper, the authors present a new approach for exactly solving chance-constrained mathematical programs having discrete distributions with finite support and random polyhedral constraints, using both decomposition and integer programming techniques to combine the results of these subproblems to yield strong valid inequalities.
Abstract: We present a new approach for exactly solving chance-constrained mathematical programs having discrete distributions with finite support and random polyhedral constraints. Such problems have been notoriously difficult to solve due to nonconvexity of the feasible region, and most available methods are only able to find provably good solutions in certain very special cases. Our approach uses both decomposition, to enable processing subproblems corresponding to one possible outcome at a time, and integer programming techniques, to combine the results of these subproblems to yield strong valid inequalities. Computational results on a chance-constrained formulation of a resource planning problem inspired by a call center staffing application indicate the approach works significantly better than both an existing mixed-integer programming formulation and a simple decomposition approach that does not use strong valid inequalities. We also demonstrate how the approach can be used to efficiently solve for a sequence of risk levels, as would be done when solving for the efficient frontier of risk and cost.
160 citations
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TL;DR: This paper examines in this paper the subset selection procedure in the context of ordinal optimization introduced in Ref. 1 with the suggestion of quantifiable subset selection sizes which are universally applicable to many simulation and modeling problems.
Abstract: We examine in this paper the subset selection procedure in the context of ordinal optimization introduced in Ref. 1. Major concepts including goal softening, selection subset, alignment probability, and ordered performance curve are formally introduced. A two-parameter model is devised to calculate alignment probabilities for a wide range of cases using two different selection rules: blind pick and horse race. Our major result includes the suggestion of quantifiable subset selection sizes which are universally applicable to many simulation and modeling problems, as demonstrated by the examples in this paper.
160 citations
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TL;DR: In this paper, three approaches are presented for generating scenario trees for 3nancial portfolio problems based on simulation, optimization and hybrid simulation/optimization.
160 citations