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dc.contributor.author
Palombarini, Jorge Andrés
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dc.contributor.author
Martínez, Ernesto Carlos
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dc.date.available
2019-02-15T17:53:39Z
dc.date.issued
2012-09
dc.identifier.citation
Palombarini, Jorge Andrés; Martínez, Ernesto Carlos; SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning; Elsevier Science Ltd; Expert Systems with Applications; 39; 11; 9-2012; 10251-10268
dc.identifier.issn
0957-4174
dc.identifier.uri
http://hdl.handle.net/11336/70284
dc.description.abstract
With the current trend towards cognitive manufacturing systems to deal with unforeseen events and disturbances that constantly demand real-time repair decisions, learning/reasoning skills and interactive capabilities are important functionalities for rescheduling a shop-floor on the fly taking into account several objectives and goal states. In this work, the automatic generation and update through learning of rescheduling knowledge using simulated transitions of abstract schedule states is proposed. Deictic representations of schedules based on focal points are used to define a repair policy which generates a goal-directed sequence of repair operators to face unplanned events and operational disturbances. An industrial example where rescheduling is needed due to the arrival of a new/rush order, or whenever raw material delay/shortage or machine breakdown events occur are discussed using the SmartGantt prototype for interactive rescheduling in real-time. SmartGantt demonstrates that due date compliance of orders-in-progress, negotiating delivery conditions of new orders and ensuring distributed production control can be dramatically improved by means of relational reinforcement learning and a deictic representation of rescheduling tasks.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science Ltd
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Automated Planning
dc.subject
Information Systems
dc.subject
Manufacturing Systems
dc.subject
Real-Time Rescheduling
dc.subject
Reinforcement Learning
dc.subject
Relational Abstractions
dc.subject.classification
Ingeniería de Sistemas y Comunicaciones
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-02-12T17:42:40Z
dc.journal.volume
39
dc.journal.number
11
dc.journal.pagination
10251-10268
dc.journal.pais
Países Bajos
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dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Palombarini, Jorge Andrés. Universidad Tecnologica Nacional. Facultad Regional Villa Maria; Argentina
dc.description.fil
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
dc.journal.title
Expert Systems with Applications
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.eswa.2012.02.176
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