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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25 Mar 2012TL;DR: This work focuses on a stochastic optimization based approach to make distributed routing and server management decisions in the context of large-scale, geographically distributed data centers, which offers significant potential for exploring power cost reductions.
Abstract: In this work we focus on a stochastic optimization based approach to make distributed routing and server management decisions in the context of large-scale, geographically distributed data centers, which offers significant potential for exploring power cost reductions. Our approach considers such decisions at different time scales and offers provable power cost and delay characteristics. The utility of our approach and its robustness are also illustrated through simulation-based experiments under delay tolerant workloads.
237 citations
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TL;DR: This paper extends the concept of minmax robustness to multi-objective optimization and calls this extension robust efficiency for uncertain multi- objective optimization problems, and uses ingredients from robust (single objective) and (deterministic) multi-Objective optimization to gain insight into the new area of robust multi- Objective optimization.
236 citations
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TL;DR: This work presents a hybrid mixed-integer disjunctive programming formulation for the stochastic program corresponding to this class of problems and hence extends the Stochastic programming framework.
Abstract: We address a class of problems where decisions have to be optimized over a time horizon given that the future is uncertain and that the optimization decisions influence the time of information discovery for a subset of the uncertain parameters. The standard approach to formulate stochastic programs is based on the assumption that the stochastic process is independent of the optimization decisions, which is not true for the class of problems under consideration. We present a hybrid mixed-integer disjunctive programming formulation for the stochastic program corresponding to this class of problems and hence extend the stochastic programming framework. A set of theoretical properties that lead to reduction in the size of the model is identified. A Lagrangean duality based branch and bound algorithm is also presented.
235 citations
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TL;DR: Risk neutral and risk averse approaches to multistage (linear) stochastic programming problems based on the Stochastic Dual Dynamic Programming (SDDP) method are discussed.
235 citations
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TL;DR: This work considers the optimal investment and operational planning of gas field developments under uncertainty in gas reserves and presents a novel stochastic programming model that incorporates the decision-dependence of the scenario tree.
233 citations