A Heuristic for Moment-Matching Scenario Generation

By Kjetil Høyland, Michal Kaut, Stein W. Wallace. Computational optimization and applications, 2003

444 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/9e397fa9e2868b3f5a15649a1c16597c59294ab3

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Citations in the corpus

Supply chain design under uncertainty using sample average approximation and dual decomposition (2009, methodology): “The method used here is a modified version of the method developed by Høyland et al. (2003). (6) The scenarios for the principal components are then transformed back into scenarios for the prediction error.”
Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network (2013, result): “This phenomenon has been observed in previous studies by Sioshansi and Short (2009) and Morales et al. (2009b) and it may result in waste of renewable energy supply, thereby undermining the benefits of renewable energy integration.”
Stability analysis of portfolio management with conditional value-at-risk (2007, methodology): “We used the method of Høyland et al. (2003) to generate scenarios of asset returns and spot currency exchange rates.”
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”
Forward Contracting and Selling Price Determination for a Retailer (2007, methodology): “Different techniques to characterize stochastic variables are available in the technical literature, e.g., time-series models [27], momentmatching [28] and [ 29 ], clustering [30] and [31], etc.”
Generation of multivariate scenario trees to model stochasticity in power management (2005, unknown): “We mention here in chronological order (i) bound-based approximationmethods [11], [8], [4], (ii) Monte Carlo-basedsampling schemes [21], [5], [2], [31], (iii) the moment-matching principle[19], [20], (iv) optimalapproximations based on probability metrics[25], [18], [7], [13], and (v) the use…”
Monte Carlo sampling-based methods for stochastic optimization (2014, unknown): “[115] and Mehrotra and Papp [160].”
Constructing bidding curves for a price-taking retailer in the norwegian electricity market (2005, methodology): “Based on the descriptive statistics presented in Tables III–V, we generated scenarios for each subset by using the method described in [ 10 ].”
Multistage Stochastic Programming: A Scenario Tree Based Approach to Planning under Uncertainty (2011, methodology): “Most deterministic methods end up with the formulation of difficult optimization problems, such as nonconvex or NP-hard problems (Høyland et al., 2003; Hochreiter & Pflug, 2007), with computationally demanding tasks (such as multidimensional integrations), especially for high-dimensional random…”