Job shop scheduling with probabilistic durations

Show simple item record Beck, J. Christopher Wilson, Nic
dc.contributor.editor de Mántaras, Ramon López
dc.contributor.editor Saitta, Lorenza 2020-11-10T13:04:46Z 2020-11-10T13:04:46Z 2004-08
dc.identifier.citation Beck, J. C. and Wilson, N. (2004) 'Job shop scheduling with probabilistic durations', ECAI'04: Proceedings of the 16th European Conference on Artificial Intelligence, 22- 27 August, Valencia, Spain: IOS Press, pp. 652–656. en
dc.identifier.startpage 652 en
dc.identifier.endpage 656 en
dc.identifier.isbn 978-1-58603-452-8
dc.description.abstract Proactive approaches to scheduling take into account information about the execution time uncertainty in forming a schedule. In this paper, we investigate proactive approaches for the job shop scheduling problem where activity durations are random variables. The main contributions are (i) the introduction of the problem of finding probabilistic execution guarantees for difficult scheduling problems; (ii) a method for generating a lower bound on the minimal makespan; (iii) the development of the Monte Carlo approach for evaluating solutions; and (iv) the design and empirical analysis of three solution techniques: an approximately complete technique, found to be computationally feasible only for very small problems, and two techniques based on finding good solutions to a deterministic scheduling problem, which scale to much larger problems. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher IOS Press en
dc.rights © 2004 IOS Press en
dc.subject Scheduling en
dc.subject Job shop scheduling problem en
dc.subject Monte Carlo approach en
dc.subject Scheduling problem en
dc.title Job shop scheduling with probabilistic durations en
dc.type Conference item en
dc.internal.authorcontactother Nic Wilson, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: en
dc.internal.availability Full text available en 2020-11-04T13:35:45Z
dc.description.version Accepted Version en
dc.internal.rssid 376747
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder ILOG, USA en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Valencia, Spain en
dc.internal.IRISemailaddress en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/00/PI.1/C075/IE/Constraint Computation: Automation and Application/ en

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