MCDM'16 - paper no. 5


 

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REMOVING INCONSISTENCY IN PAIRWISE COMPARISONS MATRIX IN THE AHP

Sławomir Jarek

Abstract:

The Analytic Hierarchy Process (AHP) allows to create a final ranking for a discrete set of decision variants on the basis of an earlier pairwise comparison of all the criteria and all the decision variants within each criterion. The properties of the obtained ranking depend on the quality of pairwise comparisons; this quality can be evaluated on the basis of consistency measured by means of certain measures. The paper discusses a mathematical model which is the foundation of the AHP and a starting point for a new method which allows to significantly reduce - and even eliminate - the inconsistency of pairwise comparisons measured by the consistency index. The proposed method allows to reduce the consistency index well below the threshold of 0.1.

Keywords:

AHP, pairwise comparison, inconsistent pairwise comparison matrices.

Reference index:

Sławomir Jarek, (2016), REMOVING INCONSISTENCY IN PAIRWISE COMPARISONS MATRIX IN THE AHP, Multiple Criteria Decision Making (11), pp. 63-76

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Scopus citations in 6 paper(s):
  1. Abbaspour, H., & Drebenstedt, C. (2020). Introducing system dynamics and analytical hierarchy process based software for selecting the best transportation system in mines. International Journal of Scientific and Technology Research, 9(3), 5648-5655
  2. Genco, F., & Genco, G. (2019). Selection of energy matrix sources in chile using a fuzzy logic decision approach. Energy Systems, doi:10.1007/s12667-019-00340-4
  3. Marnewick, C., Silvius, G., & Schipper, R. (2019). Exploring patterns of sustainability stimuli of project managers. Sustainability (Switzerland), 11(18) doi:10.3390/su11185016
  4. Mayo, F. L., & Taboada, E. B. (2020). Ranking factors affecting public transport mode choice of commuters in an urban city of a developing country using analytic hierarchy process: The case of metro cebu, philippines. Transportation Research Interdisciplinary Perspectives, doi:10.1016/j.trip.2019.100078
  5. Moons, K., Waeyenbergh, G., Pintelon, L., Timmermans, P., & De Ridder, D. (2019). Performance indicator selection for operating room supply chains: An application of ANP. Operations Research for Health Care, 23 doi:10.1016/j.orhc.2019.100229
  6. Ortiz-Barrios, M., Nugent, C., Cleland, I., Donnelly, M., & Verikas, A. (2019). Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework. Journal of Multi-Criteria Decision Analysis, doi:10.1002/mcda.1678