A method for improving cosine consistency index of pairwise comparison matrix in analytic hierarchy process
L.N.P. Kumar Rallabandi and
Ravindranath Vandrangi
International Journal of Mathematics in Operational Research, 2022, vol. 23, issue 2, 259-283
Abstract:
In analytic hierarchy process (AHP), pairwise comparison matrix (PCM) is used to rank the alternatives in the decision-making problem. The priority vector, derived from the PCM, is accepted only when the PCM is consistent. However, in many practical situations the PCM obtained by the judgments of expert may be inconsistent. Several indices have been proposed to measure the consistency level of PCM over the years and one among them is cosine consistency index (CCI). In this paper, an algorithm is proposed to improve the consistency of PCM in terms of CCI. For examining the consistency of the given PCM, the new threshold values for CCI are proposed. Correlation and regression analysis is used to derive the relation between CCI and consistency ratio (CR) from which the threshold values of CCI for PCMs of order 3 to 15 are determined. The derived regression models have been validated by using statistical techniques.
Keywords: cosine consistency index; CCI; pairwise comparison matrix; PCM; consistency ratio; analytic hierarchy process; AHP. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=127052 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmore:v:23:y:2022:i:2:p:259-283
Access Statistics for this article
More articles in International Journal of Mathematics in Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().