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

Mateos Caballero, Alfonso and Jiménez Martín, Antonio and Aguayo Garcia, Ernesto Aaron and Sabio, Pilar (2012). Preference intensity in MCDM when an additive utility function represents DM preferences. In: "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.

Description

Title: Preference intensity in MCDM when an additive utility function represents DM preferences
Author/s:
  • Mateos Caballero, Alfonso
  • Jiménez Martín, Antonio
  • Aguayo Garcia, Ernesto Aaron
  • Sabio, Pilar
Item Type: Presentation at Congress or Conference (Article)
Event Title: 10th International FLINS Conference. Uncertainty Modeling in Knowledge Engineering and Decision Making
Event Dates: 26/08/2012 - 29/08/2012
Event Location: Estambul, Turquía.
Title of Book: Uncertainty Modelling in Knowledge Engineering and Decision Making
Date: 2012
ISBN: 978-981-4417-73-0
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 19808
DC Identifier: http://oa.upm.es/19808/
OAI Identifier: oai:oa.upm.es:19808
Deposited by: Memoria Investigacion
Deposited on: 17 Oct 2013 16:02
Last Modified: 21 Apr 2016 21:20
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