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dc.date.accessioned 2012-11-09T11:46:10Z
dc.date.available 2012-11-09T11:46:10Z
dc.date.issued 2006-08
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/23927
dc.description.abstract Industrial planning has experienced notable advancements since its beginning by the middle of the 20th century. The importance of its application within the several industries where it is used has been demonstrated, regardless of the difficulty of the design of the exact algorithms that solve the variants. Heuristic methods have been applied for planning problems due to their high complexity; especially Artificial Intelligence when developing new strategies to solve one of the most important variants called task scheduling. It is possible to define task scheduling as: .a set of N production line tasks and M machines, which can execute those tasks, where the goal is to find an execution order that minimizes the accumulated execution time, known as makespan. This paper presents a GRASP meta heuristic strategy for the problem of scheduling dependent tasks in different machines en
dc.language en es
dc.subject Heuristic methods es
dc.subject Algorithms es
dc.subject Scheduling es
dc.title A GRASP algorithm to solve the problem of dependent tasks scheduling in different machines en
dc.type Objeto de conferencia es
sedici.identifier.isbn 0-387-34654-6 es
sedici.creator.person Tupia Anticona, Manuel es
sedici.description.note IFIP International Conference on Artificial Intelligence in Theory and Practice - Industrial Applications of AI es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.date.exposure 2006-08
sedici.relation.event 19 th IFIP World Computer Congress - WCC 2006 es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)