Real world decisions are characterized by uncertainty. Often, our preferences for every possible action are vague and not fully driven by rationality.
This comes true, in particular, when we have to deal with serious consequences. Probability theory, we are used to adopting in decision making processes, requires robust information to work properly.
When preliminary information is not strong enough to use probability theory, fuzzy algorythms come useful.
The example we present here (below) is a particular implementation of the min-max algorithm, from which it differs in considering the client preference factors.
The full software allows setting of the number of alternatives and their names plus the number of objectives and their names.
IMPORTANT: these alternatives may be of any kind (they are only defined by quality factor values you choose). More, quality factor types in this example are fixed (cost, performance, flexibility) but users can define their own ones in the full version.