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A New dominance measuring method for MCDM with ordinal information about DMs preferences
Research Areas
  • Operative research
Different methods based on dominance intensity measures have been recently proposed by different authors to deal with imprecision concerning MCDM problems. These methods compute dominating and dominated measures on the basis of the pairwise dominance values, which are then used in different ways to derive dominance intensity values on which the ranking of alternatives is In this paper we propose an extension of a dominance measuring method previously published by the authors to manage MCDM problems in which the decision maker? preferences are represented by ordinal information, i.e a ranking of criteria is provided based on their relative importance, whereas alternatives are also ranked in each criterion. First, optimization problems are solved to derive pairwise dominance values using the centroid function to represent the relative importance of criteria, while ordinal information about alternative in each attribute is incorporated as constraints. Once the dominance matrix is computed, we ponder the elements of the dominance matrix with the distance that exists between the mean weight vector, which is the result of each mean value from the end points of the polytope that represents the imprecision of the problem, and the optimal weight vector, that is the result of the optimization model for that element. Finally, a dominance intensity measure is computed from the pondered dominance matrix on which the ranking of alternatives is based. Monte Carlo Simulation techniques are used to compare the performance of the extension we proposed with other dominance measuring methods, with an approach proposed by Sarabando and Dias for MCDM problems with ordinal information. Two measures of efficacy are considered, the proportion of all cases in which the method selects the same best alternative as in the TRUE ranking (hit ratio) and how similar the overall alternative-ranking structures are in the TRUE and the method-driven rankings (rank-order correlation).
The 22nd International Conference on Multiple Criteria Decision Making (MCDM 2013)
Málaga Spain
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The 22nd International Conference on Multiple Criteria Decision Making, MCDM 2013
  • Autor: Ernesto Aaron Aguayo Garcia (UPM)
  • Autor: Antonio Jimenez Martin (UPM)
  • Autor: Alfonso Mateos Caballero (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de análisis de decisiones y estadística
  • Departamento: Inteligencia Artificial
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