Generating Scenario Trees for Multistage Decision Problems

By Kjetil Høyland, Stein W. Wallace. Management Sciences, 2001

738 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7df01669e9f692b81845c0419c65ecd2c10b2223

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In Computational Theory and Mathematics

In Management Science and Operations Research

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Citations in the corpus

Scenario Reduction for Futures Market Trading in Electricity Markets (2009, methodology): “In [14], the authors propose a moment matching method to generate a limited number of scenarios that satisfy specified statistical properties.”
Scenario tree modeling for multistage stochastic programs (2009, methodology): “We mention here (i) bound-based constructions [1, 8], (ii) Monte Carlo-based schemes [2, 33, 34] or Quasi Monte Carlo-based methods [22, 21], (iii) EVPI-based sampling and reduction within decomposition schemes [3], (iv) the moment-matching principle [17, 18], (v) probability metric based approximat”
Stochastic dual dynamic integer programming (2018, methodology): “Such an approximation may be constructed by Monte Carlo methods as in the sample average approximation (SAA) approach or various other constructive methods [40,44,48,64,67,78].”
Worst-Case Conditional Value-at-Risk with Application to Robust Portfolio Management (2009, methodology): “Finally, we should address that given partial information on the distribution, it is not hard to determine π 0 and π i via scenario generation, such as moment matching approaches (see Høyland and Wallace (2001) and Ji et al. (2005)).”
Risk-Constrained Bidding Strategy With Stochastic Unit Commitment (2007, methodology): “A nonlinear optimization problem was employed in [20] to generate scenario trees given the statistical properties of stochastic variables.”
Scenarios for Multistage Stochastic Programs (2000, methodology): “A simple heuristic is discussed in [ 40 ], and this area is open for more research.”
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.”
Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network (2013, neutral): “In contrast to scenario generation techniques that aim to match the moments of the underlying stochastic process such as those proposed by Hoyland and Wallace (2001) and Hoyland et al.”
A Heuristic for Moment-Matching Scenario Generation (2003, methodology): “In the first column are times for the algorithm from [6], in the second column times for the new algorithm.”
Stochastic Programming Models in Energy (2003, methodology): “An approach for scenario generation based on matching such statistical properties is described by Høyland and Wallace (2001). Alternatively, one may prefer modeling the stochastic processes as stochastic integrals, i.”
Forward Contracting and Selling Price Determination for a Retailer (2007, neutral): “, time-series models [27], momentmatching [28] and [29], clustering [30] and [31], etc.”
Approximations for Probability Distributions and Stochastic Optimization Problems (2011, methodology): “A widespread method is to match the first four moments ([12]), i.”