A new method to cope with decision makers' uncertainty in the equipment selection process

One of the main problems faced while configuring or reconfiguring manufacturing systems is to rank alternative designs taking into account all the different aspects involved (both tangible and intangible). For this purpose the Analytic Hierarchy Process (AHP) is a well-known decision making support method that addresses this problem. A major drawback of AHP is that uncertainty in the judgments of the decision makers and the resulting impact on the ranking is not considered. In real situations, however, judgments based on perceived future scenarios are almost always uncertain. To solve this problem in this paper we present the first complete probabilistic extension to the AHP method. The new method provides the decision maker not only with information on the ranking of the alternatives but also the probability that the ranking remains stable even in presence of uncertainty in the judgements. We verified the validity of the new method in a real application developed for the Ferrari racing team.