Within the area of discrete (or combinatorial) optimization, the goal is to minimize
or maximise an objective function represented by discrete decision variables.
The value that each variable may attend is limited by certain constraints.

The constraints, as well as the property of the decision variables being discrete,
establish an important fact: The number of feasible solutions is finite, although
it might be extremely large. The task is to choose the best (with respect to
the objective function) among all the feasible solutions. If the number of solutions
is very high, the problem cannot be solved without the use of modern optimization
techniques from integer programming or metaheuristics.

The Group of Optimization at SINTEF has state-of-the-art expertise on methods for solving discrete optimization problems.