Solving difficult multicommodity problems with a specialized interior-point algorithm

Due to recent advances in the development of linear programming solvers, some of the formerly considered difficult multicommodity problems can today be solved in few minutes, even faster than with specialized methods. However, for other kind of multicommodity instances, general linear solvers can still be quite inefficient. In this paper we will give an overview of the current state-of-the-art in solving large-scale multicommodity problems, comparing an specialized interior-point algorithm with CPLEX 6.5 in the solution of difficult multicommodity problems of up to 1 million of variables and 300,000 constraints.