Enhancing a Branch-and-Bound Algorithm for Two-Stage Stochastic Integer Network Design-Based Models

Authors:
Rafael Andrade;Abdel Lisser;Nelson Maculan;Gérard Plateau
Affiliations:
Laboratoire de Recherche en Informatique, Université Paris Sud, Bât 490 Université Paris Sud, 91405 Orsay Cedex, France;Laboratoire de Recherche en Informatique, Université Paris Sud, Bât 490 Université Paris Sud, 91405 Orsay Cedex, France;Universidade Federal do Rio de Janeiro, COPPE-Sistemas, C.P. 68511, 21945-970, Rio de Janeiro, Brazil;LIPN, Institut Galilée, Université Paris Nord, Avenue J.-B. Clément, 93430 Villetaneuse, France
Venue:
Management Science
Year:
2006

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Abstract

In this paper we present branch-and-bound (B&B) strategies for two-stage stochastic integer network design-based models with integrality constraints in the first-stage variables. These strategies are used within L-shaped decomposition-based B&B framework. We propose a valid inequality in order to improve B&B performance. We use this inequality to implement a multirooted B&B tree. A selective use of optimality cuts is explored in the B&B approach and we also propose a subgradient-based technique for branching on 0-1 feasible solutions. Finally, we present computational results for a fixed-charge network design problem with random demands.