  We present a decomposition method for stochastic programs with 0-1 variables
in the second-stage with absolute semi-deviation (ASD) risk measure.
Traditional stochastic programming models are risk-neutral where expected costs
are considered for the second-stage. A common approach to address risk is to
include a dispersion statistic in addition with expected costs and weighted
appropriately. Due to the lack of block angular structure, stochastic programs
with ASD risk-measure possess computational challenges. The proposed
decomposition algorithm uses another risk-measure `expected excess', and
provides tighter bounds for ASD stochastic models. We perform computational
study on a supply chain replenishment problem and standard knapsack instances.
The computational results using supply chain instances demonstrate the
usefulness of ASD risk-measure in decision making under uncertainty, and
knapsack instances indicate that the proposed methodology outperforms a direct
solver.
