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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01 Jan 2003
TL;DR: In this article, a tour of good energy optimization models that explicitly deal with uncertainty is given, where the uncertainty usually stems from unpredictability of demand and/or prices of energy, or from resource availability and prices.
Abstract: We give the reader a tour of good energy optimization models that explicitly deal with uncertainty. The uncertainty usually stems from unpredictability of demand and/or prices of energy, or from resource availability and prices. Since most energy investments or operations involve irreversible decisions, a stochastic programming approach is meaningful. Many of the models deal with electricity investments and operations, but some oil and gas applications are also presented. We consider both traditional cost minimization models and newer models that reflect industry deregulation processes. The oldest research precedes the development of linear programming, and most models within the market paradigm have not yet found their final form.
258 citations
TL;DR: In this article, sampling stochastic dynamic programming (SSDP) is used to capture the complex temporal and spatial structure of the streamflow process by using a large number of sample streamflow sequences.
Abstract: Most models for reservoir operation optimization have employed either deterministic optimization or stochastic dynamic programming algorithms. This paper develops sampling stochastic dynamic programming (SSDP), a technique that captures the complex temporal and spatial structure of the streamflow process by using a large number of sample streamflow sequences. The best inflow forecast can be included as a hydrologic state variable to improve the reservoir operating policy. A case study using the hydroelectric system on the North Fork of the Feather River in California illustrates the SSDP approach and its performance.
257 citations
TL;DR: Monte Carlo simulations demonstrate the viability of the genetic algorithm by showing that it consistently and quickly provides good feasible solutions, which makes the real time implementation for high-dimensional problems feasible.
256 citations
TL;DR: An overview of the use of Monte Carlo sampling-based methods for stochastic optimization problems with sampling is given, with the goal of introducing the topic to students and researchers and providing a practical guide for someone who needs to solve a stochastically optimization problem with sampling.
256 citations
TL;DR: In this article, a two-stage stochastic programming model is further developed by which a deterministic model for multi-period reverse logistics network design can be extended to account for the uncertainties.
Abstract: The design of reverse logistics network has attracted growing attention with the stringent pressures from environmental and social requirements In general, decisions about reverse logistics network configurations are made on a long-term basis and factors influencing such reverse logistics network design may also vary over time This paper proposes dynamic location and allocation models to cope with such issues A two-stage stochastic programming model is further developed by which a deterministic model for multiperiod reverse logistics network design can be extended to account for the uncertainties A solution approach integrating a recently proposed sampling method with a heuristic algorithm is also proposed in this research A numerical experiment is presented to demonstrate the significance of the developed stochastic model as well as the efficiency of the proposed solution method
256 citations