Robust solutions of uncertain mixed-integer linear programs using decomposition techniques
For researchers and practitioners in optimization, this method offers a more tractable approach to robust optimization problems with binary decisions and ellipsoidal uncertainty, though it is an incremental improvement over existing decomposition methods.
This paper presents a decomposition technique for solving robust mixed-integer linear programs with ellipsoidal uncertainty, improving tractability for large-scale problems and providing exact probability of constraint violation when first and second-order moments are known.
Robust optimization is a framework for modeling optimization problems involving data uncertainty and during the last decades has been an area of active research. If we focus on linear programming (LP) problems with i) uncertain data, ii) binary decisions and iii) hard constraints within an ellipsoidal uncertainty set, this paper provides a different interpretation of their robust counterpart (RC) inspired from decomposition techniques. This new interpretation allows the proposal of an ad-hoc decomposition technique to solve the RC problem with the following advantages: i) it improves tractability, specially for large-scale problems, and ii) it provides the exact probability of constraint violation in case the probability distribution of uncertain parameters are completely defined by using first and second-order probability moments. An attractive aspect of our method is that it decomposes the second-order cone programming problem, associated with the robust counterpart, into a linear master problem and different quadratically constrained problems (QCP) of considerable lower size. The optimal solution is achieved through the solution of these master and subproblems within an iterative scheme based on cutting plane approximations of the second-order cone constraints. In addition, proof of convergence of the iterative method is given.