A general algorithm for solving two-stage stochastic mixed 0–1 first-stage problems

We present an algorithmic approach for solving large-scale two-stage stochastic problems having mixed 0–1 first stage variables. The constraints in the first stage of the deterministic equivalent model have 0–1 variables and continuous variables, while the constraints in the second stage have only c...

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Bibliographic Details
Published in:Computers & operations research Vol. 36; no. 9; pp. 2590 - 2600
Main Authors: Escudero, L.F., Garín, M.A., Merino, M., Pérez, G.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.09.2009
Elsevier
Pergamon Press Inc
Subjects:
Algorithms
Applied sciences
Benders decomposition
Branch-and-fix coordination
Exact sciences and technology
Integer programming
Mathematical programming
Nonanticipativity constraints
Operational research and scientific management
Operational research. Management science
Problem solving
Splitting variables
Stochastic models
Studies
Twin node family
Two-stage stochastic integer programming
Twin node family
Two-stage stochastic integer programming
Branch-and-fix coordination
Splitting variables
Benders decomposition
Nonanticipativity constraints
Script
Partition method
Algorithmics
Cluster
Linear programming
Stochastic programming
Integer programming
Zero one programming
Deterministic model
Mixed problem
Large scale
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
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Summary:We present an algorithmic approach for solving large-scale two-stage stochastic problems having mixed 0–1 first stage variables. The constraints in the first stage of the deterministic equivalent model have 0–1 variables and continuous variables, while the constraints in the second stage have only continuous. The approach uses the twin node family concept within the algorithmic framework, the so-called branch-and-fix coordination, in order to satisfy the nonanticipativity constraints. At the same time we consider a scenario cluster Benders decomposition scheme for solving large-scale LP submodels given at each TNF integer set. Some computational results are presented to demonstrate the efficiency of the proposed approach.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2008.11.011