a full optimal solution in the case of the network with 15 nodes and 25 links with a dense traffic matrix requires a vector computer or a very long time. However, in few hours of working on a well-equipped workstation a solution with a cost within 3% from the optimum can be obtained. In practice, of course, the optimum solution is not known and the optimality gap can- not be evaluated exactly. However, observing the solution evolution for different running times it is not difficult to estimate the order of magnitude of the solution refinement versus the running time. Even accepting a certain optimality gap, networks with a number of nodes in the order of 11-13 depending on the traffic matrix and the number of links are the limit for a rea- sonable execution time. If greater networks have to be analyzed, some more important simplification has to be done. We can divide the possible simplifications in two classes: physical simplifications related to the particular nature of the problem at hand and mathematical simplifications, which is the mathematical approximation of the original ILP problem that can be applied to all the networks. 8.4.2.3.1 Physical Approximations of the ILP problem The most important class of simplifications are those aimed at reducing the number of design variables. Generally such simplifications work very well on certain types of network and traffic while they give a poor performance in some specific cases. Now we report a list of possible simplifications that has been successfully used to design large networks, but only direct experience can achieve complete confidence when used on a generic topology. A first possible simplification is to decouple the design of the MPLS layer and of the optical layer. In this case, two different ILP problems result that are both quite simpler with respect to the joined design problem. This approach is very effective when grooming has not to be used intensively to increase the effective utilization of the optical channels. In the extreme case in which no groom