Mingzheng Wang - School of Management Science and Engineering, Dalian University of Technology, Dalian 116023, China (email)
Abstract: 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.
Keywords: Stochastic nonlinear complementarity
problem, two-stage stochastic mathematical programming, sample
average approximation, super-network, convergence.
Received: November 2009; Revised: January 2011; Available Online: April 2011.
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