dc.contributor.author |
Beck, J. Christopher |
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dc.contributor.author |
Wilson, Nic |
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dc.date.accessioned |
2013-05-13T14:07:52Z |
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dc.date.available |
2013-05-13T14:07:52Z |
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dc.date.copyright |
2007 |
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dc.date.issued |
2007-07 |
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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 |
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dc.identifier.doi |
10.1613/jair.2080 |
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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 |
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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 |
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dc.subject |
Classical scheduling formulation |
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dc.subject |
Independent random variable |
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dc.subject |
Monte Carlo simulation |
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dc.subject |
Deterministic scheduling algorithm |
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dc.subject |
Probabilistic problem |
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dc.subject |
Constraint programming |
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dc.subject |
Tabu search |
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dc.subject.lcsh |
Computer science. |
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dc.title |
Proactive algorithms for job shop scheduling with probabilistic durations |
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dc.type |
Article (peer-reviewed) |
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dc.internal.authorurl |
http://research.ucc.ie/profiles/D005/nwilson |
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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 |
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dc.description.version |
Published Version |
en |
dc.internal.rssid |
727599 |
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dc.internal.wokid |
154831269 |
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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 |
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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/
|
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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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