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Self-optimizing through CBR learning

dc.contributor.authorPereira, Ivo
dc.contributor.authorMadureira, Ana Maria
dc.date.accessioned2013-04-29T11:58:28Z
dc.date.available2013-04-29T11:58:28Z
dc.date.issued2010
dc.date.updated2013-04-23T11:12:23Z
dc.description.abstractIn this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.por
dc.identifier.doi10.1109/CEC.2010.5586081
dc.identifier.isbn978-1-4244-6909-3
dc.identifier.urihttp://hdl.handle.net/10400.22/1465
dc.language.isoengpor
dc.publisherIEEEpor
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5586081por
dc.subjectSchedulingpor
dc.titleSelf-optimizing through CBR learningpor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceBarcelona, Spain, 2010por
oaire.citation.titleIEEE Congress on Evolutionary Computation (CEC)por
person.familyNamePereira
person.familyNameMadureira
person.givenNameIvo
person.givenNameAna Maria
person.identifier.ciencia-id3E18-2D4C-0E14
person.identifier.ciencia-id7F1D-5AF2-A101
person.identifier.orcid0000-0001-5440-3225
person.identifier.orcid0000-0002-0264-4710
person.identifier.ridN-1713-2016
person.identifier.ridAAH-1056-2021
person.identifier.scopus-author-id36675461900
person.identifier.scopus-author-id8634629500
rcaap.rightsclosedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication097b47eb-e9f1-40cb-9fe3-ca46efc578cb
relation.isAuthorOfPublicationcd5e5eb7-cf63-48c6-b6b9-a9db2fedebab
relation.isAuthorOfPublication.latestForDiscoverycd5e5eb7-cf63-48c6-b6b9-a9db2fedebab

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