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dc.contributor.authorTurky, A
dc.contributor.authorSabar, NR
dc.contributor.authorSattar, A
dc.contributor.authorSong, A
dc.date.accessioned2018-02-13T03:28:06Z
dc.date.available2018-02-13T03:28:06Z
dc.date.issued2017
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-68759-9_57
dc.identifier.urihttp://hdl.handle.net/10072/369281
dc.description.abstractGoogle Machine Reassignment Problem (GMRP) is a recent real world problem proposed at ROADEF/EURO challenge 2012. The aim of this problem is to maximise the usage of the available machines by reassigning processes among those machines while a numerous constraints must be not violated. In this work, we propose a great deluge algorithm with multi-neighbourhood operators (MNGD) for GMRP. Great deluge (GD) algorithm is a single solution based heuristic that accept non-improving solutions in order to escape from the local optimal point. The proposed algorithm uses multi-neighbourhood operators of various characteristics to effectively navigate the search space. The proposed algorithm is evaluated on a total of 30 instances. Computational results disclose that our proposed MNGD algorithm performed better than GD with single neighbourhood operator. Furthermore, MNGD algorithm obtains best results compared with other algorithms from the literature on some instances.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom706
dc.relation.ispartofpageto715
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.relation.ispartofvolume10593
dc.subject.fieldofresearchOther information and computing sciences not elsewhere classified
dc.subject.fieldofresearchcode469999
dc.titleMulti-neighbourhood Great Deluge for Google Machine Reassignment Problem
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorSattar, Abdul


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