Integrating large positive and negative performance differences into multicriteria majority-rule sorting models

•We extend MR-Sort to include large positive and negative performance differences.•We propose exact algorithms to elicit the parameters of the models.•The proposed approaches are tested on artificially constructed benchmarks.•The results show the performance in terms of solution quality and executio...

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Bibliographic Details
Published in:Computers & operations research 2017-05, Vol.81, p.216-230
Main Authors: Meyer, Patrick, Olteanu, Alexandru-Liviu
Format: Article
Language:English
Subjects:
Algorithms
Computer Science
Dictator
Integer programming
Mathematical analysis
Mixed-integer programming
Multi-criteria decision aiding
Multiple criteria decision making
Operations Research
Outranking relation
Sorting
Studies
Veto
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Summary:•We extend MR-Sort to include large positive and negative performance differences.•We propose exact algorithms to elicit the parameters of the models.•The proposed approaches are tested on artificially constructed benchmarks.•The results show the performance in terms of solution quality and execution time. This paper considers the issue of how to include large positive and negative differences in multicriteria sorting models, based on the outranking paradigm. We show that various outranking relations can be constructed to take into account these out- and under-performances, before presenting mathematical programs to learn the preferential parameters of these models.
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2016.11.007