  In this paper, we propose a novel decomposition approach for mixed-integer
stochastic programming (SMIP) problems that is inspired by the combination of
penalty-based Lagrangian and block Gauss-Seidel methods (PBGS). In this sense,
PBGS is developed such that the inherent decomposable structure that SMIPs
present can be exploited in a computationally efficient manner. The performance
of the proposed method is compared with the Progressive Hedging method (PH),
which also can be viewed as a Lagrangian-based method for obtaining solutions
for SMIP. Numerical experiments performed using instances from the literature
illustrate the efficiency of the proposed method in terms of computational
performance and solution quality.
