Distributionally robust discrete optimization with Entropic Value-at-Risk

We study the discrete optimization problem under the distributionally robustframework. We optimize the Entropic Value-at-Risk, which is a coherentrisk measure and is also known as Bernstein approximation for the chanceconstraint. We propose an efficient approximation algorithm to resolve theproblem via solving a sequence of nominal problems. The computationalresults show that the number of nominal problems required to be solved issmall under various distributional information sets.