The use of advanced multi-objective optimization, and of uncertainty analysis techniques , enable us to provide the decision makers with a multitude of alternatives, and to generate a large number of possible scenarios. However, the diversity of options can become an important decisional burden, generating noise and hindering both the extraction of knowledge throughout the process, and the selection of alternatives itself.

The joint employment of visual analysis techniques, classifiers and dynamic control systems, allow us to develop methods enabling users to interact with our tools, and dynamically focus the selection of alternatives in the regions of greatest interest, dismissing irrelevant solutions and avoiding unproductive efforts. With our methods it is possible to identify, in an intuitive and dynamic way, the solutions meeting the multiple conditions required to be acceptable.

We also have methods to synthesize the space of solutions. Based on cluster analysis, these methods group the solutions into a manageable number of sub-sets and then, based on the criterion of preference chosen by the decision maker, select representative solutions from each sub-set.