Learning non-monotonic additive value functions for multicriteria decision making

DOI10.1007/s00291-010-0231-2zbMath1238.90075OpenAlexW2064396797MaRDI QIDQ421079

Michael Doumpos

Publication date: 23 May 2012

Published in: OR Spectrum (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00291-010-0231-2

zbMATH Keywords

classificationmulticriteria decision makingvalue functionsevolutionary optimization

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Management decision making, including multiple objectives (90B50)

Related Items (9)

Multiple criteria decision aiding for finance: an updated bibliographic survey ⋮ Expressiveness and robustness measures for the evaluation of an additive value function in multiple criteria preference disaggregation methods: an experimental analysis ⋮ Compromise solutions for stochastic multicriteria acceptability analysis with uncertain preferences and nonmonotonic criteria ⋮ A MIP-based approach to learn MR-sort models with single-peaked preferences ⋮ Multicriteria decision aid models for the prediction of securities class actions: evidence from the banking sector ⋮ Preferential reducts and constructs in robust multiple criteria ranking and sorting ⋮ Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria ⋮ A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding ⋮ Preference disaggregation for multiple criteria sorting with partial monotonicity constraints: application to exposure management of nanomaterials

Uses Software

• M-MACBETH
• UTA Plus

Cites Work

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• MACBETH — An interactive path towards the construction of cardinal value functions
• Rough sets theory for multicriteria decision analysis
• Preference disaggregation: 20 years of MCDA experience.

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