Sample average approximation method for stochastic complementarity problems with applications to supply chain supernetworks
Mingzheng Wang, M. Montaz Ali, Gui-Hua Lin · Journal of Industrial and Management Optimization · 2011
We consider a class of stochastic nonlinear complementarityproblems. We propose a new reformulation of the stochasticcomplementarity problem, that is, a two-stage stochasticmathematical programming model reformulation. Based on thisreformulation, we propose a smoothing-based sample averageapproximation method for stochastic complementarity problem andprove its convergence. As an application, a supply chainsuper-network equilibrium is modeled as a stochastic nonlinearcomplementarity problem and numerical results on the problem arereported.