  We study sample average approximations (SAA) of chance constrained programs.
SAA methods typically approximate the actual distribution in the chance
constraint using an empirical distribution constructed from random samples
assumed to be independent and identically distributed according to the actual
distribution. In this paper, we consider a nonstationary variant of this
problem, where the random samples are assumed to be independently drawn in a
sequential fashion from an unknown and possibly time-varying distribution. This
nonstationarity may be driven by changing environmental conditions present in
many real-world applications. To account for the potential nonstationarity in
the data generation process, we propose a novel robust SAA method exploiting
information about the Wasserstein distance between the sequence of
data-generating distributions and the actual chance constraint distribution. As
a key result, we obtain distribution-free estimates of the sample size required
to ensure that the robust SAA method will yield solutions that are feasible for
the chance constraint under the actual distribution with high confidence.
