A Tractable Approach for designing Piecewise Affine Policies in Two-stage Adjustable Robust Optimization

We consider the problem of designing piecewise affine policies for two-stage adjustable robust linear optimization problems under right-hand side uncertainty. It is well known that a piecewise affine policy is optimal although the number of pieces can be exponentially large. A significant challenge in designing a practical piecewise affine policy is constructing good pieces of the uncertainty set. Here we address this challenge by introducing a new framework in which the uncertainty set is “approximated” by a “dominating” simplex. The corresponding policy is then based on a mapping from the uncertainty set to the simplex. Although our piecewise affine policy has exponentially many pieces, it can be computed efficiently by solving a compact linear program given the dominating simplex. Furthermore, we can find the dominating simplex in a closed form if the uncertainty set satisfies some symmetries and can be computed using a MIP in general. The performance of our policy is significantly better than the affine policy for many important uncertainty sets, such as ellipsoids and norm-balls, both theoretically and numerically. For instance, for hypersphere uncertainty set, our piecewise affine policy can be computed by an LP and gives a O(m)-approximation whereas the affine policy requires us to solve a second order cone program and has a worst-case performance bound of O( √ m). Aharon Ben-Tal Industrial Engineering and Management, Technion Israel Institute of Technology and CentER, Tilburg University, Netherlands E-mail: [email protected] Omar El Housni, Vineet Goyal Industrial Engineering and Operations Research, Columbia University E-mail: {oe2148, vg2277}@columbia.edu 2 A. Ben-Tal, O. El Housni, V. Goyal