Scenario tree modeling for multistage stochastic programs

By Holger Heitsch, Werner Römisch. Mathematical programming, 2009

337 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/bba79b5e3b0921970c04f9effb327d1cbfa90ee7

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In Electrical and Electronic Engineering

In Management Science and Operations Research

In Ocean Engineering

Citations in the corpus

Multistage Stochastic Programming: A Scenario Tree Based Approach to Planning under Uncertainty (2011, background): “…stochastic programming, was initially believed to be easily extended to the multistage case (Heitsch & Römisch, 2003); but then it turned out that more elaborated measures of probability distances, integrating the intertemporal aspect of observations, were needed (Heitsch & Römisch, 2009).”
Distributionally Robust Optimization: A Review (2019, methodology): “[268] study continuous and discrete scenario reduction [97, 139, 140, 141, 6], where p-Wasserstein metric utilizing `p-norm is used as a measure of discrepancy between distributions.”
Sampling strategies and stopping criteria for stochastic dual dynamic programming: a case study in long-term hydrothermal scheduling (2011, methodology): “Other approaches to construct scenario trees have been discussed in the literature (see, for instance [ 12 ] for a recent review) but here we focus exclusively on sampling method since we can rely on the statistical inference provided in [35].”
Monte Carlo sampling-based methods for stochastic optimization (2014, background): “[72], Heitsch and Römisch [96, 97], Pflug and Pichler [192] and references therein for further discussions on this type of methods.”
The 1/ N investment strategy is optimal under high model ambiguity (2012, background): “One reason for choosing the Kantorovich distance is that it plays an important role in stability results in stochastic programming, see for example Mirkov and Pflug (2007); Heitsch and Römisch (2009).”
A Fast Solution Method for Stochastic Transmission Expansion Planning (2017, methodology): “Existing scenario reduction methods include clustering algorithms such as K-means [15], backward and forward selection algorithms [11], [12], and their variation or extension [11], [16], heuristic-based methods [14], [17], etc.”
Lectures on Stochastic Programming: Modeling and Theory (2009, methodology): “The Reader is referred to Kall and Mayer [122], Heitsch and Römisch [98], Hochreiter and Pflug [109], Casey and Sen [35], Pennanen [176], Dupačova, Growe-Kuska and Römisch [71], and the references therein.”