An exact algorithm for linear integer programming problems with distributionally robust chance constraints.

The distributionally robust chance constrained integer programming problems are notoriously hard to solve due to the stochastic feature and the discrete nature of integer variables. In this paper, an exact solution method is developed for linear integer programming problems with the distributionally robust chance constraint. By exploring the geometric properties, some domain cut techniques based on feasible points and infeasible points are derived. Thus cut off some sub-boxes which do not contain any optimal solution and the optimality gap is reduced successively in the solution iterations. Then the optimal solution will be found in a finite number of iterations. Encouraging computational results are also reported in the paper. [ABSTRACT FROM AUTHOR]