In this paper we study solution methods for solving the dual problem corresponding to the Lagrangian Decomposition of two-stage stochastic mixed 0-1 models. We represent the two-stage stochastic mixed 0-1 problem by a splitting variable representation of the deterministic equivalent model, where 0-1 and continuous variables appear at any stage. Lagrangian Decomposition (LD) is proposed for satisfying both the integrality constraints for the 0-1 variables and the non-anticipativity constraints. We compare the performance of four iterative algorithms based on dual Lagrangian Decomposition schemes: the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm, and the Dynamic Constrained Cutting Plane scheme. We test the tightness of the LD bounds in a testbed of medium- and large-scale stochastic instances. © 2011 Sociedad de Estadística e Investigación Operativa.
CITATION STYLE
Escudero, L. F., Garín, M. A., Pérez, G., & Unzueta, A. (2012). Lagrangian Decomposition for large-scale two-stage stochastic mixed 0-1 problems. TOP, 20(2), 347–374. https://doi.org/10.1007/s11750-011-0237-1
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