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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Bibliographic Details

Published in:	Computational management science Vol. 21; no. 1; p. 32
Main Authors:	Zhang, Hongyu, Grossmann, Ignacio E., Tomasgard, Asgeir
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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