Show simple item record

dc.contributor.authorKharbeche, M.
dc.contributor.authorCarlier, J.
dc.contributor.authorHaouari, Mohamed
dc.contributor.authorMoukrim, A.
dc.date.accessioned2013-10-26T13:36:38Z
dc.date.available2013-10-26T13:36:38Z
dc.date.issued2011-06
dc.identifier.issn1936-6582
dc.identifier.urihttp://hdl.handle.net/10679/299
dc.identifier.urihttp://link.springer.com/article/10.1007%2Fs10696-011-9079-2#
dc.description.abstractThis paper investigates an exact method for the Robotic Cell Problem. We present a branch-and-bound algorithm which is the first exact procedure specifically designed with regard to this complex flow shop scheduling variant. Also, we propose a new mathematical programming model as well as new lower bounds. Furthermore, we describe an effective genetic algorithm that includes, as a mutation operator, a local search procedure. We report the results of a computational study that provides evidence that medium-sized instances, with up to 176 operations, can be optimally solved. Also, we found that the new proposed lower bounds outperform lower bounds from the literature. Finally, we show, that the genetic algorithm delivers good solutions while requiring short CPU times.en_US
dc.language.isoengen_US
dc.publisherSpringer Science+Business Mediaen_US
dc.relation.ispartofFlexible Services and Manufacturing Journal
dc.rightsrestrictedAccess
dc.titleExact methods for the robotic cell problemen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0003-0767-8220 & YÖK ID ) Haouari, Mohamed
dc.contributor.ozuauthorHaouari, Mohamed
dc.identifier.volume23
dc.identifier.issue2
dc.identifier.startpage242
dc.identifier.endpage261
dc.identifier.wosWOS:000293790700008
dc.identifier.doi10.1007/s10696-011-9079-2
dc.subject.keywordsRobotic cellen_US
dc.subject.keywordsFlow shop with transportation times and blockingen_US
dc.subject.keywordsBranch-and-bounden_US
dc.subject.keywordsLower boundsen_US
dc.subject.keywordsGenetic algorithmsen_US
dc.identifier.scopusSCOPUS:2-s2.0-79960565314
dc.contributor.authorMale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record


Share this page