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

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  • Ghaderi, Mohammad
  • Ruiz, Francisco
  • Agell, Núria

Abstract

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.

Suggested Citation

  • Ghaderi, Mohammad & Ruiz, Francisco & Agell, Núria, 2017. "A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1073-1084.
    • Handle: RePEc:eee:ejores:v:259:y:2017:i:3:p:1073-1084
    DOI: 10.1016/j.ejor.2016.11.038
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