Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach

Autores
Novara, Franco Matías; Henning, Gabriela Patricia
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Uncertainty and variability are inherent characteristics of industrial environments that affect production schedules and could turn them unfeasible or economically unattractive. Process-inherent uncertainty, one of the multiple stochasticity sources, influences task processing times, transforming them into one of the main uncertain parameters. This work proposes a scheduling approach to consider this situation in a proactive fashion at the decision stage, without resorting to the generation of scenarios. It relies on a Constraint Programming (CP) model that focusses on the Capacity Constrained Stage (CCS) to reduce its complexity and size. The proposal is tested with various instances of three case studies, showing that the approach is computationally efficient. The attained agendas can effectively absorb the processing times variabilities in an efficient way, exhibiting a resilient behaviour. To assess the schedules, they are compared with those reached by a deterministic CP formulation and with the agendas obtained by means of another proactive methodology proposed by the same authors.
Fil: Novara, Franco Matías. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina
Fil: Henning, Gabriela Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Materia
CONSTRAINT PROGRAMMING
MULTIPRODUCT MULTISTAGE BATCH PLANTS
SCHEDULING
THEORY OF CONSTRAINTS
UNCERTAIN PROCESSING TIMES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/86622
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Resilient scheduling under uncertain processing times: a hybrid CP/TOC approachNovara, Franco MatíasHenning, Gabriela PatriciaCONSTRAINT PROGRAMMINGMULTIPRODUCT MULTISTAGE BATCH PLANTSSCHEDULINGTHEORY OF CONSTRAINTSUNCERTAIN PROCESSING TIMEShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Uncertainty and variability are inherent characteristics of industrial environments that affect production schedules and could turn them unfeasible or economically unattractive. Process-inherent uncertainty, one of the multiple stochasticity sources, influences task processing times, transforming them into one of the main uncertain parameters. This work proposes a scheduling approach to consider this situation in a proactive fashion at the decision stage, without resorting to the generation of scenarios. It relies on a Constraint Programming (CP) model that focusses on the Capacity Constrained Stage (CCS) to reduce its complexity and size. The proposal is tested with various instances of three case studies, showing that the approach is computationally efficient. The attained agendas can effectively absorb the processing times variabilities in an efficient way, exhibiting a resilient behaviour. To assess the schedules, they are compared with those reached by a deterministic CP formulation and with the agendas obtained by means of another proactive methodology proposed by the same authors.Fil: Novara, Franco Matías. Universidad Nacional del Litoral. Facultad de Ingeniería Química; ArgentinaFil: Henning, Gabriela Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaElsevier B.V.2018-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/86622Novara, Franco Matías; Henning, Gabriela Patricia; Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach; Elsevier B.V.; Computer Aided Chemical Engineering; 44; 1-2018; 1261-12661570-7946CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/B978-0-444-64241-7.50205-6info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:51:44Zoai:ri.conicet.gov.ar:11336/86622instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:51:44.583CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach
title Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach
spellingShingle Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach
Novara, Franco Matías
CONSTRAINT PROGRAMMING
MULTIPRODUCT MULTISTAGE BATCH PLANTS
SCHEDULING
THEORY OF CONSTRAINTS
UNCERTAIN PROCESSING TIMES
title_short Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach
title_full Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach
title_fullStr Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach
title_full_unstemmed Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach
title_sort Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach
dc.creator.none.fl_str_mv Novara, Franco Matías
Henning, Gabriela Patricia
author Novara, Franco Matías
author_facet Novara, Franco Matías
Henning, Gabriela Patricia
author_role author
author2 Henning, Gabriela Patricia
author2_role author
dc.subject.none.fl_str_mv CONSTRAINT PROGRAMMING
MULTIPRODUCT MULTISTAGE BATCH PLANTS
SCHEDULING
THEORY OF CONSTRAINTS
UNCERTAIN PROCESSING TIMES
topic CONSTRAINT PROGRAMMING
MULTIPRODUCT MULTISTAGE BATCH PLANTS
SCHEDULING
THEORY OF CONSTRAINTS
UNCERTAIN PROCESSING TIMES
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Uncertainty and variability are inherent characteristics of industrial environments that affect production schedules and could turn them unfeasible or economically unattractive. Process-inherent uncertainty, one of the multiple stochasticity sources, influences task processing times, transforming them into one of the main uncertain parameters. This work proposes a scheduling approach to consider this situation in a proactive fashion at the decision stage, without resorting to the generation of scenarios. It relies on a Constraint Programming (CP) model that focusses on the Capacity Constrained Stage (CCS) to reduce its complexity and size. The proposal is tested with various instances of three case studies, showing that the approach is computationally efficient. The attained agendas can effectively absorb the processing times variabilities in an efficient way, exhibiting a resilient behaviour. To assess the schedules, they are compared with those reached by a deterministic CP formulation and with the agendas obtained by means of another proactive methodology proposed by the same authors.
Fil: Novara, Franco Matías. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina
Fil: Henning, Gabriela Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
description Uncertainty and variability are inherent characteristics of industrial environments that affect production schedules and could turn them unfeasible or economically unattractive. Process-inherent uncertainty, one of the multiple stochasticity sources, influences task processing times, transforming them into one of the main uncertain parameters. This work proposes a scheduling approach to consider this situation in a proactive fashion at the decision stage, without resorting to the generation of scenarios. It relies on a Constraint Programming (CP) model that focusses on the Capacity Constrained Stage (CCS) to reduce its complexity and size. The proposal is tested with various instances of three case studies, showing that the approach is computationally efficient. The attained agendas can effectively absorb the processing times variabilities in an efficient way, exhibiting a resilient behaviour. To assess the schedules, they are compared with those reached by a deterministic CP formulation and with the agendas obtained by means of another proactive methodology proposed by the same authors.
publishDate 2018
dc.date.none.fl_str_mv 2018-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/86622
Novara, Franco Matías; Henning, Gabriela Patricia; Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach; Elsevier B.V.; Computer Aided Chemical Engineering; 44; 1-2018; 1261-1266
1570-7946
CONICET Digital
CONICET
url http://hdl.handle.net/11336/86622
identifier_str_mv Novara, Franco Matías; Henning, Gabriela Patricia; Resilient scheduling under uncertain processing times: a hybrid CP/TOC approach; Elsevier B.V.; Computer Aided Chemical Engineering; 44; 1-2018; 1261-1266
1570-7946
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/B978-0-444-64241-7.50205-6
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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