Decomposition methods for multi-horizon stochastic programming

Multi-horizon stochastic programming includes short-term and long-term uncertainty in investment planning problems more efficiently than traditional multi-stage stochastic programming. In this paper, we exploit the block separable structure of multi-horizon stochastic linear programming, and establi...

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Vydané v:	Computational management science Ročník 21; číslo 1; s. 32
Hlavní autori:	Zhang, Hongyu, Grossmann, Ignacio E., Tomasgard, Asgeir
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024 Springer Nature B.V
Predmet:	Algorithms Benders decomposition Business and Management Decomposition Linear programming Operations Research/Decision Theory Optimization Original Paper Stochastic programming Primal reduction Parallel computing Lagrangean decomposition Benders decomposition Multi-horizon stochastic programming Stochastic programming
ISSN:	1619-697X, 1619-6988
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