For stochastic mixed-integer programs, we revisit the dual decomposition algorithm of Care and Schultz from a computational perspective with the aim of its parallelization. We address an important bottleneck of parallel execution by identifying a formulation that permits the parallel solution of the master program by using structure-exploiting interior-point solvers. Our results demonstrate the potential for parallel speedup and the importance of regularization (stabilization) in the dual optimization. Load imbalance is identified as a remaining barrier to parallel scalability.

%B Operations Research Letters %V 41 %8 05/2013 %G eng %U http://www.sciencedirect.com/science/article/pii/S0167637713000242 %N 3 %1 http://www.mcs.anl.gov/papers/P3037-0912.pdf %& 252-258