A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding

Mohammad Ghaderi, Francisco Ruiz, Núria Agell

Producció científica: Article en revista indexadaArticleAvaluat per experts

39 Cites (Scopus)

Resum

A new framework for preference disaggregation in multiple criteria decision aiding is introduced. The proposed approach aims to infer non-monotonic additive preference models from a set of indirect pairwise comparisons. The preference model is presented as a set of marginal value functions and the discriminatory power of the inferred preference model is maximized against its complexity. To infer a value function that is compatible with the supplied preference information, the proposed methodology leads to a linear programming optimization problem that is easy to solve. The applicability and effectiveness of the new methodology is demonstrated in a thorough experimental analysis covering a broad range of decision problems.

Idioma originalAnglès
Pàgines (de-a)1073-1084
Nombre de pàgines12
RevistaEuropean Journal of Operational Research
Volum259
Número3
DOIs
Estat de la publicacióPublicada - 16 de juny 2017
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