Scenario generation with distribution functions and correlations

In this paper, we present a method for generating scenarios for two-stage stochastic programs,
using multivariate distributions specified by their marginal distributions and the correlation
matrix. The margins are described by their cumulative distribution functions and we
allow each margin to be of different type. We demonstrate the method on a model from stochastic
service network design and show that it improves the stability of the scenario-generation
process, compared to both sampling and a method that matches moments and correlations.