Scenarios for Multistage Stochastic Programs

By Jitka Dupacová, Giorgio Consigli, Stein W. Wallace. 2000

533 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “In fact, data reduction is most similar to the scenario reduction methods in the stochastic programming literature (see e.g., Dupačová et al., 2000, 2003).”
Power management in a hydro-thermal system under uncertainty by Lagrangian relaxation (2002, unknown): “E-mail:nicole@mathematik.hu-berlin.de.ySystems Research Institute, Warsaw, Poland.”
Evaluation of scenario-generation methods for stochastic programming (2007, methodology): “Examples of these methods can be found in [8, 19].”
A Bilevel Stochastic Programming Approach for Retailer Futures Market Trading (2009, background): “, time series models [22], clustering [23], etc.”
Risk-Constrained Bidding Strategy With Stochastic Unit Commitment (2007, background): “A detailed literature review on scenario generation was presented in [21].”
The design of robust value-creating supply chain networks (2010, background): “Scenario generation techniques were also proposed for multi-stage programs (Ducapova et al., 2000; Hoyland and Wallace, 2002).”
Scenario reduction in stochastic programming (2003, unknown): “For more information on recent work for scenario generation and reduction we refer to [ 9 ].”
Generation of multivariate scenario trees to model stochasticity in power management (2005, background): “During the last few years scenario modelling and the generation of scenario trees became a very active field of research in stochastic programming (see the survey [6]).”
Scenario tree modeling for multistage stochastic programs (2009, background): “We also mention the importance of evaluating the quality of scenario trees and of a postoptimality analysis [4, 19].”
Multi-Stage Stochastic Programming With Nonanticipativity Constraints for Expansion of Combined Power and Natural Gas Systems (2018, methodology): “scenario tree uses Monte Carlo techniques to generate scenarios by conditional sampling [26]–[29].”
Monte Carlo sampling-based methods for stochastic optimization (2014, methodology): “[71]) and moment-matching techniques, see for instance Høyland and Wallace [114], Høyland et al.”
Wind Power Scenario Generation and Reduction in Stochastic Programming Framework (2013, methodology): “Monte Carlo simulation with the path-based scenario-generation method has been used for generation of electric load scenarios in power management problems [8].”
Forward Contracting and Selling Price Determination for a Retailer (2007, methodology): “, time-series models [27], momentmatching [28] and [29], clustering [30] and [31], etc.”
Scenario Reduction for Futures Market Trading in Electricity Markets (2009, background): “The generation of scenarios for multistage stochastic problems is described in [20].”