Abstract
We demonstrate how large-scale stochastic linear programs can be efficiently solved by using a blending of classical decomposition and a relatively new technique called importance sampling. We discuss an adaptive importance sampling scheme using an additive approximation function. We show how this technique can be applied to facility expansion planning of electrical power systems. Numerical results of testproblems with numerous stochastic parameters are presented.
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