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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
2019-02-15T17:13:55Z
dc.date.issued
2011-12
dc.identifier.citation
Palombarini, Jorge Andrés; Martínez, Ernesto Carlos; Real-time rescheduling of production systems using relational reinforcement learning; QUALIS CAPES (UFSC); Iberoamerican Journal of Industrial Engineering; 3; 2; 12-2011; 136-153
dc.identifier.issn
2175-8018
dc.identifier.uri
http://hdl.handle.net/11336/70280
dc.description.abstract
Most scheduling methodologies developed until now have laid down good theoretical foundations, but there is still the need for real-time rescheduling methods that can work effectively in disruption management. In this work, a novel approach for automatic generation of rescheduling knowledge using Relational Reinforcement Learning (RRL) is presented. Relational representations of schedule states and repair operators enable to encode in a compact way and use in real-time rescheduling knowledge learned through intensive simulations of state transitions. An industrial example where a current schedule must be repaired following the arrival of a new order is discussed using a prototype application – SmartGantt®- for interactive rescheduling in a reactive way. SmartGantt® demonstrates the advantages of resorting to RRL and abstract states for real-time rescheduling. A small number of training episodes are required to define a repair policy which can handle on the fly events such as order insertion, resource break-down, raw material delay or shortage and rush order arrivals using a sequence of operators to achieve a selected goal.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
QUALIS CAPES (UFSC)
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Reinforcement Learning
dc.subject
Rescheduling
dc.subject
Production Systems
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
Real-time rescheduling of production systems using 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
2019-02-12T17:27:44Z
dc.journal.volume
3
dc.journal.number
2
dc.journal.pagination
136-153
dc.journal.pais
Brasil
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dc.journal.ciudad
Florianipolis
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
Iberoamerican Journal of Industrial Engineering
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://periodicos.incubadora.ufsc.br/index.php/IJIE/article/view/1568
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