Consistency issues of interval pairwise comparison matrices
In this paper, we investigate the consistency issues of interval pairwise comparison matrices in detail. Using logarithmic Manhattan distance to define the deviation degree of a pairwise comparison matrix to consistent pairwise comparison matrices, we propose a new consistency index of pairwise comparison matrices. Based on this consistency index of pairwise comparison matrices, we develop a consistency index of interval pairwise comparison matrices. Several desired properties of the proposed consistency indexes are presented. Furthermore, linear programming (LP) models are developed to compute the consistency indexes. Then, we propose a LP-based consistency improving model, which optimally preserves original pairwise comparison information in improving consistency. Meanwhile, considering the uncertainty plays an important role in the consistency index of interval pairwise comparison matrices, this consistency improving model is extended to simultaneously manage the uncertain degree in interval pairwise comparison matrices. Finally, we discuss the consistency-based prioritization method, and propose the strong consistency index of interval pairwise comparison matrices.
consistency linear programming multiple attribute decision making interval pairwise comparison matrix