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Inferring efficient weights from pairwise comparison matrices

Opened Access Inferring efficient weights from pairwise comparison matrices

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Autor: Blanquero Bravo, Rafael
Carrizosa Priego, Emilio José
Conde Sánchez, Eduardo
Departamento: Universidad de Sevilla. Departamento de Estadística e Investigación Operativa
Fecha: 2006-10
Publicado en: Mathematical Methods of Operations Research, 64 (2), 271-284.
Tipo de documento: Artículo
Resumen: Several multi-criteria-decision-making methodologies assume the existence of weights associated with the different criteria, reflecting their relative importance.One of the most popular ways to infer such weights is the analytic hierarchy process, which constructs first a matrix of pairwise comparisons, from which weights are derived following one out of many existing procedures, such as the eigenvector method or the least (logarithmic) squares. Since different procedures yield different results (weights) we pose the problem of describing the set of weights obtained by “sensible” methods: those which are efficient for the (vector-) optimization problem of simultaneous minimization of discrepancies. A characterization of the set of efficient solutions is given, which enables us to assert that the least-logarithmic-squares solution is always efficient, whereas the (widely used) eigenvector solution is not, in some cases, efficient, thus its use in practice may be questionable.
Cita: Blanquero Bravo, R., Carrizosa Priego, E.J. y Conde Sánchez, E. (2006). Inferring efficient weights from pairwise comparison matrices. Mathematical Methods of Operations Research, 64 (2), 271-284.
Tamaño: 224.2Kb
Formato: PDF

URI: http://hdl.handle.net/11441/47872

DOI: 10.1007/s00186-006-0077-1

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