Proactive algorithms for job shop scheduling with probabilistic durations

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dc.contributor.author Beck, J. Christopher
dc.contributor.author Wilson, Nic
dc.date.accessioned 2013-05-13T14:07:52Z
dc.date.available 2013-05-13T14:07:52Z
dc.date.copyright 2007
dc.date.issued 2007-07
dc.identifier.citation Beck, 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.2080 en
dc.identifier.volume 28 en
dc.identifier.startpage 183 en
dc.identifier.endpage 232 en
dc.identifier.uri http://hdl.handle.net/10468/1119
dc.identifier.doi 10.1613/jair.2080
dc.description.abstract Most 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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Association for the Advancement of Artificial Intelligence (AAAI) en
dc.relation.uri http://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.html en
dc.subject Classical scheduling formulation en
dc.subject Independent random variable en
dc.subject Monte Carlo simulation en
dc.subject Deterministic scheduling algorithm en
dc.subject Probabilistic problem en
dc.subject Constraint programming en
dc.subject Tabu search en
dc.subject.lcsh Computer science. en
dc.title Proactive algorithms for job shop scheduling with probabilistic durations en
dc.type Article (peer-reviewed) en
dc.internal.authorurl http://research.ucc.ie/profiles/D005/nwilson en
dc.internal.authorcontactother Nic Wilson, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: n.wilson@4c.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2012-12-20T17:42:54Z
dc.description.version Published Version en
dc.internal.rssid 727599
dc.internal.wokid 154831269
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Journal of Artificial Intelligence Research en
dc.internal.copyrightchecked No. CORA - ROMEO en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress n.wilson@ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/00/PI.1/C075/IE/Constraint Computation: Automation and Application/
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Technology and Innovation Development Award (TIDA)/05/IN.1/I886 TIDA 09/IE/Costraint Baset Energy Cost Efficient Scheduling/


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