 A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aidingMohammad Ghaderi, Francisco Ruiz and Núria Agell European Journal of Operational Research, 2017, vol. 259, issue 3, 1073-1084 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. Keywords: Multiple criteria analysis; Preference disaggregation; Decision analysis; Linear programming; Non-monotonic value functions (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:259:y:2017:i:3:p:1073-1084 DOI: 10.1016/j.ejor.2016.11.038 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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