Dominance Measuring Approach using Stochastic Weights

Mateos Caballero, Alfonso; Jiménez Martín, Antonio; Moreno Díaz, Arminda y Aguayo Garcia, Ernesto Aaron (2010). Dominance Measuring Approach using Stochastic Weights. En: "25th Mini-EURO Conference Uncertainty and Robustness in Planning and Decision Making, URPDM 2010", 15/04/2010 - 17/04/2010, Coimbra, Portugal. ISBN 978-989-95055-3-7.

Descripción

Título: Dominance Measuring Approach using Stochastic Weights
Autor/es:
  • Mateos Caballero, Alfonso
  • Jiménez Martín, Antonio
  • Moreno Díaz, Arminda
  • Aguayo Garcia, Ernesto Aaron
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 25th Mini-EURO Conference Uncertainty and Robustness in Planning and Decision Making, URPDM 2010
Fechas del Evento: 15/04/2010 - 17/04/2010
Lugar del Evento: Coimbra, Portugal
Título del Libro: CD-ROM Proceedings of the 25th Mini-EURO Conference Uncertainty and Robustness in Planning and Decision Making, URPDM 2010
Fecha: 2010
ISBN: 978-989-95055-3-7
Materias:
Palabras Clave Informales: Additive multi-attribute utility function, imprecise weights, Monte Carlo simulation, dominance measures.
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

In this paper we propose an approach to obtain a ranking of 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 multi-attribute utility function, in which weights are modeled by independent normal variables, the performance in each attribute for each alternative is an interval value and classes of utility functions are available for each attribute. The approach we propose is based on dominance measures, which are computed in a similar way that when the imprecision concerning weights is modeled by uniform distributions or by an ordinal relation. In this paper we will show how the approach can be applied when the imprecision concerning weights are represented by normal distributions. Extensions to other distributions, such as truncated normal or beta, can be feasible using Monte Carlo simulation techniques.

Más información

ID de Registro: 7601
Identificador DC: http://oa.upm.es/7601/
Identificador OAI: oai:oa.upm.es:7601
URL Oficial: http://www.inescc.pt/urpdm2010/program.html
Depositado por: Memoria Investigacion
Depositado el: 05 Jul 2011 10:59
Ultima Modificación: 20 Abr 2016 16:41
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