Fuzzy C-means-based scenario bundling for stochastic service network
  design
  Stochastic service network designs with uncertain demand represented by a set
of scenarios can be modelled as a large-scale two-stage stochastic
mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a
decomposition method for solving the resulting SMIP. The computational
performance of the PHA can be greatly enhanced by decomposing according to
scenario bundles instead of individual scenarios. At the heart of bundle-based
decomposition is the method for grouping the scenarios into bundles. In this
paper, we present a fuzzy c-means-based scenario bundling method to address
this problem. Rather than full membership of a bundle, which is typically the
case in existing scenario bundling strategies such as k-means, a scenario has
partial membership in each of the bundles and can be assigned to more than one
bundle in our method.
