  This paper studies a distributionally robust chance constrained program
(DRCCP) with Wasserstein ambiguity set, where the uncertain constraints should
be satisfied with a probability at least a given threshold for all the
probability distributions of the uncertain parameters within a chosen
Wasserstein distance from an empirical distribution. In this work, we
investigate equivalent reformulations and approximations of such problems. We
first show that a DRCCP can be reformulated as a conditional value-at-risk
constrained optimization problem, and thus admits tight inner and outer
approximations. We also show that a DRCCP of bounded feasible region is mixed
integer representable by introducing big-M coefficients and additional binary
variables. For a DRCCP with pure binary decision variables, by exploring the
submodular structure, we show that it admits a big-M free formulation, which
can be solved by a branch and cut algorithm. Finally, we present a numerical
study to illustrate the effectiveness of the proposed formulations.
