An Ordinal Consistency Indicator for Pairwise Comparison Matrix

The pairwise comparison (PC) matrix is often used to manifest human judgments, and it has been successfully applied in the analytic hierarchy process (AHP). As a PC matrix is formed by making paired reciprocal comparisons, symmetry is a striking characteristic of a PC matrix. It is this simple but powerful means of resolving multicriteria decision-making problems that is the basis of AHP; however, in practical applications, human judgments may be inconsistent. Although Saaty’s rule for the consistency test is commonly accepted, there is evidence that those so-called “acceptable” PC matrices may not be ordinally consistent, which is a necessary condition for a PC matrix to be accepted. We propose an ordinal consistency indicator called SDR (standard deviation of ranks), derive the upper bound of the SDR, suggest a threshold for a decision-maker to assess whether the ordinal consistency of a PC matrix is acceptable, and reveal a surprising fact that the degree of ordinal inconsistency of a small PC matrix may be more serious than a large one. We made a comparative analysis with some other indicators. Experimental results showed that the ordinal inconsistency measured by the SDR is invariant under heterogeneous judgment measurements with a varied spectrum of scales, and that the SDR is superior to the two compared indicators. Note that the SDR not only works for a multiplicative PC matrix but can also be used for additive and fuzzy PC matrices.

Risk Identification in Bridge Construction Stage Based on Fuzzy Hierarchy Analysis

Due to the characteristic of ordinal consistency in the analytical hierarchy process (AHP) difficult to meet, the method combined AHP and the theory of fuzzy mathematics is introduced, the idea of fuzzy analytic hierarchy process is introduced innovatively. The failure mode and the main factor of failure induced during construction of bridge engineering are identified. The method can exert the brain advantage of evaluation, and simplify the computation process.

ADJUSTING ORDINAL AND CARDINAL INCONSISTENCIES IN DECISION PREFERENCES BASED ON GOWER PLOTS

Gower plots provide a powerful graphical tool to detect cardinal and ordinal inconsistencies in a pairwise preference matrix. However, there is no systematical way to help a decision maker to reduce these inconsistencies. This paper develops a model that can assist in making a consistent decision. Gower plots are used to detect major inconsistencies. A multi-objective program is then formulated to adjust both the cardinal inconsistencies and the preference changes subjected to the constraints of ordinal consistency.