Robust binary optimization using a safe tractable approximation

We present a robust optimization approach to 0-1 linear programming with uncertain objective coefficients based on a safe tractable approximation of chance constraints, when only the first two moments and the support of the random parameters is known. We obtain nonlinear problems with only one additional (continuous) variable. The resulting robust optimization problems can be interpreted as a nominal problem with modified coefficients. We compare our approach with Bertsimas and Sim [1]. In numerical experiments, we obtain solutions of similar quality in faster time.