Proactive algorithms for job shop scheduling with probabilistic durations

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dc.contributor.authorBeck, J. Christopher
dc.contributor.authorWilson, Nic
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2013-05-13T14:07:52Z
dc.date.available2013-05-13T14:07:52Z
dc.date.copyright2007
dc.date.issued2007-07
dc.date.updated2012-12-20T17:42:54Z
dc.description.abstractMost classical scheduling formulations assume a fixed and known duration for each activity. In this paper, we weaken this assumption, requiring instead that each duration can be represented by an independent random variable with a known mean and variance. The best solutions are ones which have a high probability of achieving a good makespan. We first create a theoretical framework, formally showing how Monte Carlo simulation can be combined with deterministic scheduling algorithms to solve this problem. We propose an associated deterministic scheduling problem whose solution is proved, under certain conditions, to be a lower bound for the probabilistic problem. We then propose and investigate a number of techniques for solving such problems based on combinations of Monte Carlo simulation, solutions to the associated deterministic problem, and either constraint programming or tabu search. Our empirical results demonstrate that a combination of the use of the associated deterministic problem and Monte Carlo simulation results in algorithms that scale best both in terms of problem size and uncertainty. Further experiments point to the correlation between the quality of the deterministic solution and the quality of the probabilistic solution as a major factor responsible for this success.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBeck, JC, Wilson, N; (2007) 'Proactive Algorithms For Job Shop Scheduling With Probabilistic Durations'. Journal of Artificial Intelligence Research, 28 :183-232. doi: 10.1613/jair.2080en
dc.identifier.doi10.1613/jair.2080
dc.identifier.endpage232en
dc.identifier.journaltitleJournal of Artificial Intelligence Researchen
dc.identifier.startpage183en
dc.identifier.urihttps://hdl.handle.net/10468/1119
dc.identifier.volume28en
dc.language.isoenen
dc.publisherAssociation for the Advancement of Artificial Intelligence (AAAI)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/00/PI.1/C075/IE/Constraint Computation: Automation and Application/
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Technology and Innovation Development Award (TIDA)/05/IN.1/I886 TIDA 09/IE/Costraint Baset Energy Cost Efficient Scheduling/
dc.relation.urihttp://www.jair.org/papers/paper2080.html
dc.rights© Copyright 1993-2009 AI Access Foundation, Inc. The AAAI electronic version of this article can be found at http://www.jair.org/papers/paper2080.htmlen
dc.subjectClassical scheduling formulationen
dc.subjectIndependent random variableen
dc.subjectMonte Carlo simulationen
dc.subjectDeterministic scheduling algorithmen
dc.subjectProbabilistic problemen
dc.subjectConstraint programmingen
dc.subjectTabu searchen
dc.subject.lcshComputer science.en
dc.titleProactive algorithms for job shop scheduling with probabilistic durationsen
dc.typeArticle (peer-reviewed)en
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