Multicriteria decision analysis with minimum information: combining data envelopment analysis with multi attribute value theory

In this paper we use some basic principles from data envelopment analysis in order to extract the necessary information for solving a multicriteria decision analysis problem. The proposed method (enhanced alternative cross-evaluation, ACE+) is appropriate when either the decision maker is unwilling (or hardly available) to provide information, or there are several decision makers, each one supporting his/her own option. It is similar to the AXE method of Doyle, but it goes one step further: each alternative uses its most favourable weights (as in AXE) and its most favourable value functions in order to perform a self evaluation, according to multi attribute value theory. These self-evaluations are averaged in order to derive the overall peer-evaluation for each alternative. The minimum information required from the decision maker is to define the weight interval for each criterion. Beside the peer evaluation and the final rating of the alternatives, the method provides useful conclusions for the sensitivity analysis of the results.