The MCDA methods component offers a set of techniques that provide guidance and coherence to the decision making process. These techniques (SAW/TOPSIS and ELECTRE methods) can be broadly categorized as compensatory and noncompensatory, being also different regarding the preference information required from the DM and the type of output provided. A full description of the MCDA methods and implementation details utilized by MCPUIS are beyond the scope of this paper. The interested reader is referred to Yoon and Hwang [23] or Triantaphyllou [20] for an introduction/overview of these techniques. The three MCDA methods presently included require the DM to assign weights to the various criteria, to be carried out using a meaningful methodology for weights elicitation, and that the performances of each alternative according to each criterion are in numerical and comparable values (i.e. normalized). SAW and TOPSIS belong to the group of complete aggregation methods that compute an aggregate performance for each alternative. TOPSIS ranks the alternatives based upon DM determined weights on the criteria, and each alternative's performance vis a vis two reference points: “ideal solution” and “anti-ideal solution” (e.g., see [4,6,14]) – this particular feature distinguishes this method from the other two methods.