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Sample average approximation method for stochastic complementarity problems with applications to supply chain supernetworks

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  • 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.
    Mathematics Subject Classification: Primary: 90C33, 90C30, 90C15.


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