Analytics-based decomposition of a class of bilevel problems

dc.contributor.authorFajemisin, Adejuyigbe
dc.contributor.authorCliment, Laura
dc.contributor.authorPrestwich, Steven D.
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2019-07-19T11:12:01Z
dc.date.available2019-07-19T11:12:01Z
dc.date.issued2019-06-15
dc.date.updated2019-07-18T11:36:25Z
dc.description.abstractThis paper proposes a new class of multi-follower bilevel problems. In this class the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. The new class is formalised and compared with existing problems in the literature. We show that approaches currently in use for solving multi-follower problems are unsuitable for this class. Evolutionary algorithms can be used, but these are computationally intensive and do not scale up well. Instead we propose an analytics-based decomposition approach. Two example problems are solved using our approach and two evolutionary algorithms, and the decomposition approach produces much better and faster results as the problem size increases.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationFajemisin A., Climent L. and Prestwich S. D. (2019) ) ‘Analytics-based decomposition of a class of bilevel problems’, in Le Thi, H., Le, H. and Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. World Congress on Global Optimization 2019. Advances in Intelligent Systems and Computing, 991, pp. 617-626. doi: 10.1007/978-3-030-21803-4_62en
dc.identifier.doi10.1007/978-3-030-21803-4_62en
dc.identifier.endpage626en
dc.identifier.isbn978-3-030-21802-7
dc.identifier.isbn978-3-030-21803-4
dc.identifier.journaltitleAdvances in Intelligent Systems and Computingen
dc.identifier.startpage617en
dc.identifier.urihttps://hdl.handle.net/10468/8209
dc.identifier.volume991en
dc.language.isoenen
dc.publisherSpringer Nature Switzerland AGen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://link.springer.com/chapter/10.1007%2F978-3-030-21803-4_62
dc.rights© 2019, Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of a paper published in Le Thi H., Le H., Pham Dinh T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, 991. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-21803-4_62en
dc.subjectBilevelen
dc.subjectAnalyticsen
dc.subjectClusteringen
dc.subjectDecompositionen
dc.titleAnalytics-based decomposition of a class of bilevel problemsen
dc.typeConference itemen
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