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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Shrnutí: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 establish that it can be decomposed by Benders decomposition and Lagrangean decomposition. In addition, we propose parallel Lagrangean decomposition with primal reduction that, (1) solves the scenario subproblems in parallel, (2) reduces the primal problem by keeping one copy for each scenario group at each stage, and (3) solves the reduced primal problem in parallel. We apply the parallel Lagrangean decomposition with primal reduction, Lagrangean decomposition and Benders decomposition to solve a stochastic energy system investment planning problem. The computational results show that: (a) the Lagrangean type decomposition algorithms have better convergence at the first iterations to Benders decomposition, and (b) parallel Lagrangean decomposition with primal reduction is very efficient for solving multi-horizon stochastic programming problems. Based on the computational results, the choice of algorithms for multi-horizon stochastic programming is discussed.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1619-697X
1619-6988
DOI:10.1007/s10287-024-00509-y