Wang, Mingzheng; Ali, M. Montaz; Lin, Guihua Sample average approximation method for stochastic complementarity problems with applications to supply chain supernetworks. (English) Zbl 1225.90138 J. Ind. Manag. Optim. 7, No. 2, 317-345 (2011). Summary: We consider a class of stochastic nonlinear complementarity problems. We propose a new reformulation of the stochastic complementarity problem, that is, a two-stage stochastic mathematical programming model reformulation. Based on this reformulation, we propose a smoothing-based sample average approximation method for stochastic complementarity problem and prove its convergence. As an application, a supply chain super-network equilibrium is modeled as a stochastic nonlinear complementarity problem and numerical results on the problem are reported. Cited in 5 Documents MSC: 90C33 Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) 90C30 Nonlinear programming 90C15 Stochastic programming Keywords:stochastic nonlinear complementarity problem; two-stage stochastic mathematical programming; sample average approximation; super-network; convergence PDFBibTeX XMLCite \textit{M. Wang} et al., J. Ind. Manag. Optim. 7, No. 2, 317--345 (2011; Zbl 1225.90138) Full Text: DOI