A modified fuzzy logarithmic least squares method for fuzzy analytic hierarchy process

This paper revisits the fuzzy logarithmic least squares method (LLSM) in the analytic hierarchy process and points out its incorrectness in the normalization of local fuzzy weights, infeasibility in deriving the local fuzzy weights of a fuzzy comparison matrix when the lower bound value of a non-normalized fuzzy weight turns out to be greater than its upper bound value, uncertainty of local fuzzy weights for incomplete fuzzy comparison matrices, and unreality of global fuzzy weights. A modified fuzzy LLSM, which is formulated as a constrained nonlinear optimization model, is therefore suggested to tackle all these problems. A numerical example is examined to show the applicability of the modified fuzzy LLSM and its advantages.