Multiple attribute decision analysis with imprecise information

An advantage of multiple attribute decision analysis is its capability of handling nonmonetary factors in addition to traditional monetary data. However, measurements of nonmonetary attributes are often highly subjective and imprecise and most analytical methods lack provisions for handling imprecise data. In this paper the authors reexamine the linear additive utility model, expand and adapt it to accommodate imprecise attribute ratings and weights.