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Learning non-monotonic additive value functions for multicriteria decision making

Michael Doumpos

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URI: http://purl.tuc.gr/dl/dias/12BF23F1-F497-4B2E-AD41-2AECE83CC8B6
Year 2012
Type of Item Peer-Reviewed Journal Publication
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Bibliographic Citation M. Doumpos, "Learning non-monotonic additive value functions for multicriteria decision making," OR Spectr., vol. 34, no. 1, pp. 89-106, Jan. 2012. doi:10.1007/s00291-010-0231-2 https://doi.org/10.1007/s00291-010-0231-2
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Summary

Multiattribute additive value functions constitute an important class of models for multicriteria decision making. Such models are often used to rank a set of alternatives or to classify them into pre-defined groups. Preference disaggregation techniques have been used to construct additive value models using linear programming techniques based on the assumption of monotonic preferences. This paper presents a methodology to construct non-monotonic value function models, using an evolutionary optimization approach. The methodology is implemented for the construction of multicriteria models that can be used to classify the alternatives in pre-defined groups, with an application to credit rating.

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