Preference intensity in MCDM when an additive utility function represents DM preferences

Mateos Caballero, Alfonso; Jiménez Martín, Antonio; Aguayo Garcia, Ernesto Aaron y Sabio, Pilar (2012). Preference intensity in MCDM when an additive utility function represents DM preferences. En: "10th International FLINS Conference. Uncertainty Modeling in Knowledge Engineering and Decision Making", 26/08/2012 - 29/08/2012, Estambul, Turquía.. ISBN 978-981-4417-73-0. pp. 106-111.

Descripción

Título: Preference intensity in MCDM when an additive utility function represents DM preferences
Autor/es:
  • Mateos Caballero, Alfonso
  • Jiménez Martín, Antonio
  • Aguayo Garcia, Ernesto Aaron
  • Sabio, Pilar
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 10th International FLINS Conference. Uncertainty Modeling in Knowledge Engineering and Decision Making
Fechas del Evento: 26/08/2012 - 29/08/2012
Lugar del Evento: Estambul, Turquía.
Título del Libro: Uncertainty Modelling in Knowledge Engineering and Decision Making
Fecha: 2012
ISBN: 978-981-4417-73-0
Materias:
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

We propose a new method for ranking alternatives in multicriteria decision-making problems when there is imprecision concerning the alternative performances, component utility functions and weights. We assume decision maker?s preferences are represented by an additive multiattribute utility function, in which weights can be modeled by independent normal variables, fuzzy numbers, value intervals or by an ordinal relation. The approaches are based on dominance measures or exploring the weight space in order to describe which ratings would make each alternative the preferred one. On the one hand, the approaches based on dominance measures compute the minimum utility difference among pairs of alternatives. Then, they compute a measure by which to rank the alternatives. On the other hand, the approaches based on exploring the weight space compute confidence factors describing the reliability of the analysis. These methods are compared using Monte Carlo simulation.

Más información

ID de Registro: 19808
Identificador DC: http://oa.upm.es/19808/
Identificador OAI: oai:oa.upm.es:19808
Depositado por: Memoria Investigacion
Depositado el: 17 Oct 2013 16:02
Ultima Modificación: 21 Abr 2016 21:20
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