  In this paper, we consider a network capacity expansion problem in the
context of telecommunication networks, where there is uncertainty associated
with the expected traffic demand. We employ a distributionally robust
stochastic optimization (DRSO) framework where the ambiguity set of the
uncertain demand distribution is constructed using the moments information, the
mean and variance. The resulting DRSO problem is formulated as a bilevel
optimization problem. We develop an efficient solution algorithm for this
problem by characterizing the resulting worst-case two-point distribution,
which allows us to reformulate the original problem as a convex optimization
problem.
  In computational experiments the performance of this approach is compared to
that of the robust optimization approach with a discrete uncertainty set. The
results show that solutions from the DRSO model outperform the robust
optimization approach on highly risk-averse performance metrics, whereas the
robust solution is better on the less risk-averse metric.
