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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
2021-03-12T13:45:51Z
dc.date.issued
2019-07
dc.identifier.citation
Palombarini, Jorge Andrés; Martínez, Ernesto Carlos; Closed-loop rescheduling using deep reinforcement learning; Elsevier B.V.; IFAC-PapersOnLine; 52; 1; 7-2019; 231-236
dc.identifier.uri
http://hdl.handle.net/11336/128207
dc.description.abstract
Modern socio-technical manufacturing systems are characterized by high levels of variability which gives rise to poor predictability of environmental conditions at the shop-floor. Therefore, a closed-loop rescheduling strategy for handling unforeseen events and unplanned disturbances has become a key element for any real-time disruption management strategy in order to guarantee highly efficient production in uncertain and dynamic conditions. In this work, a real-time rescheduling task is modeled as a closed-loop control problem in which an artificial intelligent agent implements control knowledge generated offline using a schedule simulator to learn schedule repair policies directly from high-dimensional sensory inputs. The rescheduling control knowledge is stored in a deep Q-network, which is used closed-loop to select repair actions to achieve a small set of repaired goal states. The network is trained using the deep Q-learning algorithm with experience replay over a variety of simulated transitions between schedule states based on color-rich Gantt chart images and negligible prior knowledge as inputs. An industrial example is discussed to highlight that the proposed approach enables end-to-end learning of comprehensive rescheduling policies and encoding plant-specific knowledge that can be understood by human experts.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier B.V.
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DEEP REINFORCEMENT LEARNING
dc.subject
MANUFACTURING SYSTEMS
dc.subject
REAL-TIME RESCHEDULING
dc.subject
UNCERTAINTY
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
Closed-loop rescheduling using deep 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
2021-03-05T18:52:58Z
dc.identifier.eissn
2405-8963
dc.journal.volume
52
dc.journal.number
1
dc.journal.pagination
231-236
dc.journal.pais
Países Bajos
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dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Palombarini, Jorge Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones y Transferencia de Villa María. Universidad Nacional de Villa María. Centro de Investigaciones y Transferencia de Villa María; 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
IFAC-PapersOnLine
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2405896319301521
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.ifacol.2019.06.067
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