Analytics-based decomposition of a class of bilevel problems

Show simple item record

dc.contributor.author Fajemisin, Adejuyigbe
dc.contributor.author Climent, Laura
dc.contributor.author Prestwich, Steven D.
dc.date.accessioned 2019-07-19T11:12:01Z
dc.date.available 2019-07-19T11:12:01Z
dc.date.issued 2019-06-15
dc.identifier.citation Fajemisin 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_62 en
dc.identifier.volume 991 en
dc.identifier.startpage 617 en
dc.identifier.endpage 626 en
dc.identifier.isbn 978-3-030-21802-7
dc.identifier.isbn 978-3-030-21803-4
dc.identifier.uri http://hdl.handle.net/10468/8209
dc.identifier.doi 10.1007/978-3-030-21803-4_62 en
dc.description.abstract This 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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Springer Nature Switzerland AG en
dc.relation.uri https://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_62 en
dc.subject Bilevel en
dc.subject Analytics en
dc.subject Clustering en
dc.subject Decomposition en
dc.title Analytics-based decomposition of a class of bilevel problems en
dc.type Conference item en
dc.internal.authorcontactother Steven David Prestwich, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: s.prestwich@cs.ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 12 months after publication by request of the publisher. en
dc.check.date 2020-06-15
dc.date.updated 2019-07-18T11:36:25Z
dc.description.version Accepted Version en
dc.internal.rssid 493288140
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Advances in Intelligent Systems and Computing en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Metz, France en
dc.internal.IRISemailaddress s.prestwich@cs.ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement