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

Absolute semi-deviation risk measure for ordering problem with transportation cost in Supply Chain