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Abstract
This paper offers a new procedure for ranking multicritena fuzzy alternatives when the decision-maker subscribes to the notion of ‘the larger, the better’. For each alternative a joint membership function captures possible interactions among ratings for each criterion. The ranking procedure first orthogonally projects the joint membership functions from the multicritena decision space to the one-dimensional preference subspace, and then the fuzzy projections are ranked in that subspace. A method for generating joint membership functions is introduced, and a numerical example is presented.