Decision makers have often to face different, and usually conflicting, objectives. In these cases, the aggregation of multiple criteria into a single objective function seems a natural approach, but the rules for aggregation may be arbitrary and the resulting problem may be oversimplified.

Providing the decision maker with a single “optimal” solution may be not adequate to capture the complexity of the actual decision to be made. In contrast, a shortlist of “non-dominated” solutions, i.e. solutions that cannot be improved in one objective without deteriorating at least another one, may effectively support the decision process. With this respect, multi-objective optimization tools are of interest.