Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29229
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dc.contributor.authorReid, Kenneth Nen_UK
dc.contributor.authorLi, Jingpengen_UK
dc.contributor.authorBrownlee, Alexanderen_UK
dc.contributor.authorKern, Mathiasen_UK
dc.contributor.authorVeerapen, Nadarajenen_UK
dc.contributor.authorSwan, Jerryen_UK
dc.contributor.authorOwusu, Gilberten_UK
dc.date.accessioned2019-04-05T00:04:00Z-
dc.date.available2019-04-05T00:04:00Z-
dc.date.issued2019en_UK
dc.identifier.urihttp://hdl.handle.net/1893/29229-
dc.description.abstractEmployee scheduling problems are of critical importance to large businesses. These problems are hard to solve due to large numbers of conflicting constraints. While many approaches address a subset of these constraints, there is no single approach for simultaneously addressing all of them. We hybridise 'Evolutionary Ruin & Stochastic Recreate' and 'Variable Neighbourhood Search' metaheuristics to solve a real world instance of the employee scheduling problem to near optimality. We compare this with Simulated Annealing, exploring the algorithm configuration space using the irace software package to ensure fair comparison. The hybrid algorithm generates schedules that reduce unmet demand by over 28% compared to the baseline. All data used, where possible, is either directly from the real world engineer scheduling operation of around 25,000 employees , or synthesised from a related distribution where data is unavailable.en_UK
dc.language.isoenen_UK
dc.publisherACMen_UK
dc.relationReid KN, Li J, Brownlee A, Kern M, Veerapen N, Swan J & Owusu G (2019) A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference 2019. GECCO '19: The Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic, 13.07.2019-17.07.2019. New York: ACM, pp. 1311-1318. https://doi.org/10.1145/3321707.3321769en_UK
dc.rights[Paper__3___Author_Copy.pdf] This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. © ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the Genetic and Evolutionary Computation Conference 2019 http://doi.acm.org/10.1145/3321707.3321769en_UK
dc.rights[Paper__3___ER_SR___VNS.pdf] The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectEvolutionary Ruin and Stochastic Recreate, Metaheuristics, Employee Scheduling, Variable Neighbourhood Searchen_UK
dc.titleA Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problemen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2019-07-31en_UK
dc.rights.embargoreason[Paper__3___ER_SR___VNS.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.rights.embargoreason[Paper__3___Author_Copy.pdf] Until this work is published there will be an embargo on the full text of this work.en_UK
dc.identifier.doi10.1145/3321707.3321769en_UK
dc.citation.spage1311en_UK
dc.citation.epage1318en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderEPSRC Engineering and Physical Sciences Research Councilen_UK
dc.author.emailknr1@cs.stir.ac.uken_UK
dc.citation.btitleProceedings of the Genetic and Evolutionary Computation Conference 2019en_UK
dc.citation.conferencedates2019-07-13 - 2019-07-17en_UK
dc.citation.conferencelocationPrague, Czech Republicen_UK
dc.citation.conferencenameGECCO '19: The Genetic and Evolutionary Computation Conference 2019en_UK
dc.citation.isbn978-1-4503-6111-8en_UK
dc.publisher.addressNew Yorken_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationBT Group Plcen_UK
dc.contributor.affiliationLille University of Science & Technology (University of Lille 1)en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationBT Group Plcen_UK
dc.identifier.wtid1259864en_UK
dc.contributor.orcid0000-0001-8654-2430en_UK
dc.contributor.orcid0000-0002-6758-0084en_UK
dc.contributor.orcid0000-0003-2892-5059en_UK
dc.date.accepted2019-03-21en_UK
dcterms.dateAccepted2019-03-21en_UK
dc.date.filedepositdate2019-04-03en_UK
dc.relation.funderprojectDAASE: Dynamic Adaptive Automated Software Engineeringen_UK
dc.relation.funderrefEP/J017515/1en_UK
dc.subject.tagEmployee Rosteringen_UK
dc.subject.tagEmployee Schedulingen_UK
dc.subject.tagMetaheuristicsen_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
rioxxterms.versionAMen_UK
local.rioxx.authorReid, Kenneth N|0000-0001-8654-2430en_UK
local.rioxx.authorLi, Jingpeng|0000-0002-6758-0084en_UK
local.rioxx.authorBrownlee, Alexander|0000-0003-2892-5059en_UK
local.rioxx.authorKern, Mathias|en_UK
local.rioxx.authorVeerapen, Nadarajen|en_UK
local.rioxx.authorSwan, Jerry|en_UK
local.rioxx.authorOwusu, Gilbert|en_UK
local.rioxx.projectEP/J017515/1|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.freetoreaddate2019-07-31en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2019-07-31en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/all-rights-reserved|2019-07-31|en_UK
local.rioxx.filenamePaper__3___Author_Copy.pdfen_UK
local.rioxx.filecount2en_UK
local.rioxx.source978-1-4503-6111-8en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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