dc.creatorTramontina G.B.
dc.creatorWainer J.
dc.creatorEllis C.
dc.date2004
dc.date2015-06-26T14:25:35Z
dc.date2015-11-26T14:15:14Z
dc.date2015-06-26T14:25:35Z
dc.date2015-11-26T14:15:14Z
dc.date.accessioned2018-03-28T21:16:08Z
dc.date.available2018-03-28T21:16:08Z
dc.identifier
dc.identifierProceedings Of The Acm Symposium On Applied Computing. , v. 2, n. , p. 1396 - 1403, 2004.
dc.identifier
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-2442514401&partnerID=40&md5=28f1ecaae29d45cb657a1c89f9159cb4
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/94796
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/94796
dc.identifier2-s2.0-2442514401
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1242746
dc.descriptionOrdering the cases in a workflow can result in significant decrease on the number of late jobs. But merging workflow and scheduling is not trivial. This paper presents some of the problems of using scheduling results in ordering cases in a workflow and tackles two of them: the uncertainties on the cases' processing times and routing. A new approach to modeling these uncertainties is also proposed: the guess and solve technique. It consists of making a guess on the execution times and routes the case will follow, and solving the corresponding deterministic scheduling problem using a suitable technique, in this paper genetic algorithms. Simulation results show that for almost all workloads rules such as earliest due date first, and guess and solve (if the error in guessing is bound by 30%) are statistically significantly better than the commonly used FIFO rule regarding the number of late jobs.
dc.description2
dc.description
dc.description1396
dc.description1403
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dc.languageen
dc.publisher
dc.relationProceedings of the ACM Symposium on Applied Computing
dc.rightsfechado
dc.sourceScopus
dc.titleApplying Scheduling Techniques To Minimize The Number Of Late Jobs In Workflow Systems
dc.typeActas de congresos


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