Absolute semi-deviation risk measure for ordering problem with transportation cost in Supply Chain
Abstract
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.
Cite
@article{arxiv.1605.08391,
title = {Absolute semi-deviation risk measure for ordering problem with transportation cost in Supply Chain},
author = {Saravanan Venkatachalam and Lewis Ntaimo},
journal= {arXiv preprint arXiv:1605.08391},
year = {2016}
}