Relative Importance Modeling in the Presence of Uncertainty and Interdependent Metrics

Abstract

Modern systems design problems are characterized by the necessity to identify and enable multiple tradeoffs. This can be accomplished by transformation of the design problem to a multiple objective optimization formulation. However, existing multiple criteria techniques can lead to unattractive solutions due to their assumption of independent decision criteria. This, combined with static relative importance values, can quickly lead MCDM to unattractive design solutions. These problems are magnified by uncertainty in the relative importance due to the high potential for variability in these numbers. This paper presents a discussion and application of Multiple Criteria Decision Making (MCDM) to a systems design problem and discusses the uncertainties in modern techniques. It then identifies a strategy for minimizing the effects of uncertainty and interdependence by suggesting a two-part relative importance model for each criterion. This model builds on the authors’ previous work on the subject by including an interdependence correction as well as a method to simulate uncertainty in the user-specified relative importance.