  We study distributionally robust chance-constrained programs (DRCCPs) with
individual chance constraints under a Wasserstein ambiguity. The DRCCPs treat
the risk tolerances associated with the distributionally robust chance
constraints (DRCCs) as decision variables to trade off between the system cost
and risk of violations by penalizing the risk tolerances in the objective
function. The introduction of adjustable risks, unfortunately, leads to NP-hard
optimization problems. We develop integer programming approaches for individual
chance constraints with uncertainty either on the right-hand side or on the
left-hand side. In particular, we derive mixed integer programming
reformulations for the two types of uncertainty to determine the optimal risk
tolerance for the chance constraint. Valid inequalities are derived to
strengthen the formulations. We test diverse instances of diverse sizes.
