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 published on a yearly basis
Papers
More filters
••
TL;DR: A study in which an optimal operating policy for a multipurpose reservoir was determined, where the optimal operatingpolicy is stated in terms of the state of the reservoir indicated by the storage volume and the river flow in the preceding month and uses a stochastic dynamic programming approach.
Abstract: . For a multipurpose single reservoir a deterministic optimal operating policy can be readily devised by the dynamic programming method. However, this method can only be applied to sets of deterministic stream flows as might be used repetitively in a Monte Carlo study or possibly in a historical study. This paper reports a study in which an optimal operating policy for a multipurpose reservoir was determined, where the optimal operating policy is stated in terms of the state of the reservoir indicated by the storage volume and the river flow in the preceding month and uses a stochastic dynamic programming approach. Such a policy could be implemented in real time operation on a monthly basis or it could be used in a design study. As contrasted with deterministic dynamic programming, this method avoids the artificiality of using a single set of stream flows. The data for this study are the conditional probabilities of the stream flow in successive months, the physical features of the reservoir in question, and the return functions and constraints under which the system operates.
101 citations
••
TL;DR: A new splitting approach is developed to these models, optimality conditions and duality theory, which is used to construct special decomposition methods for stochastic dominance constraints of second order.
Abstract: We consider a new class of optimization problems involving stochastic dominance constraints of second order. We develop a new splitting approach to these models, optimality conditions and duality theory. These results are used to construct special decomposition methods.
101 citations
••
TL;DR: In this paper, the authors consider a two-stage stochastic programming procedure in which the performance function to be optimized is replaced by its empirical mean, and the convergence rate for the probability of deviation of the empirical optimum from the true optimum is established using large deviation techniques.
Abstract: This paper considers a procedure of two-stage stochastic programming in which the performance function to be optimized is replaced by its empirical mean. This procedure converts a stochastic optimization problem into a deterministic one for which many methods are available. Another strength of the method is that there is essentially no requirement on the distribution of the random variables involved. Exponential convergence for the probability of deviation of the empirical optimum from the true optimum is established using large deviation techniques. Explicit bounds on the convergence rates are obtained for the case of quadratic performance functions. Finally, numerical results are presented for the famous news vendor problem, which lends experimental evidence supporting exponential convergence.
101 citations
•
21 Mar 2013TL;DR: This paper presents a meta-modelling framework for efficient and scalable dynamic programming in optimal reservoir operation for large-scale reservoir system operation and describes its applications in water resources management and flood control.
Abstract: 1. Water resources management 2. Incremental dynamic programming in optimal reservoir operation 3. Stochastic dynamic programming in optimal reservoir operation 4. Optimal reservoir operation for water quality 5. Large-scale reservoir system operation 6. Optimal reservoir operation for flood control References Index.
101 citations
••
TL;DR: In this paper, a power portfolio optimization model that is intended as a decision aid for scheduling and hedging (DASH) in the wholesale power market is proposed, which integrates the unit commitment model with financial decision making by including the forwards and spot market activity within the scheduling decision model.
Abstract: We consider a power portfolio optimization model that is intended as a decision aid for scheduling and hedging (DASH) in the wholesale power market. Our multiscale model integrates the unit commitment model with financial decision making by including the forwards and spot market activity within the scheduling decision model. The methodology is based on a multiscale stochastic programming model that selects portfolio positions that perform well on a variety of scenarios generated through statistical modeling and optimization. When compared with several commonly used fixed-mix policies, our experiments demonstrate that the DASH model provides significant advantages.
101 citations