Dominance Measuring Approach using Stochastic Weights

Mateos Caballero, Alfonso and Jiménez Martín, Antonio and Moreno Díaz, Arminda and Aguayo Garcia, Ernesto Aaron (2010). Dominance Measuring Approach using Stochastic Weights. In: "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.

Description

Title: Dominance Measuring Approach using Stochastic Weights
Author/s:
  • Mateos Caballero, Alfonso
  • Jiménez Martín, Antonio
  • Moreno Díaz, Arminda
  • Aguayo Garcia, Ernesto Aaron
Item Type: Presentation at Congress or Conference (Article)
Event Title: 25th Mini-EURO Conference Uncertainty and Robustness in Planning and Decision Making, URPDM 2010
Event Dates: 15/04/2010 - 17/04/2010
Event Location: Coimbra, Portugal
Title of Book: CD-ROM Proceedings of the 25th Mini-EURO Conference Uncertainty and Robustness in Planning and Decision Making, URPDM 2010
Date: 2010
ISBN: 978-989-95055-3-7
Subjects:
Freetext Keywords: Additive multi-attribute utility function, imprecise weights, Monte Carlo simulation, dominance measures.
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 7601
DC Identifier: http://oa.upm.es/7601/
OAI Identifier: oai:oa.upm.es:7601
Official URL: http://www.inescc.pt/urpdm2010/program.html
Deposited by: Memoria Investigacion
Deposited on: 05 Jul 2011 10:59
Last Modified: 20 Apr 2016 16:41
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