Knowledge representation in Industry 4.0 Scheduling problems

Autores
Rossit, Daniel Alejandro; Tohmé, Fernando Abel
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Industry 4.0 raises the need for a closer integration of management systems in manufacturing companies. Such process is driven by Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Starting from the potential of these technologies, a knowledge architecture aimed at addressing scheduling problems is proposed. Scheduling-support systems generally do not solve real-world scheduling problems, being instead only capable of solving simplified versions, producing solutions that human schedulers adapt to real problems. The architecture aims to record and consolidate the empirical knowledge generated by the solutions of actual scheduling problems. In this way, it summarizes the implicit criteria used by human schedulers. The architecture presented here records this knowledge in data structures compatible with the structure of scheduling problems. In further iterations this knowledge crystallizes into a sound and smart structure.
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina
Materia
CYBER-PHYSICAL SYSTEMS
INDUSTRY 4.0
SCHEDULING
DECISIONAL DNA
KNOWLEDGE REPRESENTATION
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:11336/165422
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Knowledge representation in Industry 4.0 Scheduling problemsRossit, Daniel AlejandroTohmé, Fernando AbelCYBER-PHYSICAL SYSTEMSINDUSTRY 4.0SCHEDULINGDECISIONAL DNAKNOWLEDGE REPRESENTATIONhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Industry 4.0 raises the need for a closer integration of management systems in manufacturing companies. Such process is driven by Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Starting from the potential of these technologies, a knowledge architecture aimed at addressing scheduling problems is proposed. Scheduling-support systems generally do not solve real-world scheduling problems, being instead only capable of solving simplified versions, producing solutions that human schedulers adapt to real problems. The architecture aims to record and consolidate the empirical knowledge generated by the solutions of actual scheduling problems. In this way, it summarizes the implicit criteria used by human schedulers. The architecture presented here records this knowledge in data structures compatible with the structure of scheduling problems. In further iterations this knowledge crystallizes into a sound and smart structure.Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaTaylor & Francis Ltd2022-01-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/165422Rossit, Daniel Alejandro; Tohmé, Fernando Abel; Knowledge representation in Industry 4.0 Scheduling problems; Taylor & Francis Ltd; International Journal Of Computer Integrated Manufacturing; 2022; 10-1-2022; 5-250951-192XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/action/journalInformation?journalCode=tcim20info:eu-repo/semantics/altIdentifier/url/https://doi.org/10.1080/0951192X.2021.2022760info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:54:12Zoai:ri.conicet.gov.ar:11336/165422instacron: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:34982025-10-15 14:54:13.161CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Knowledge representation in Industry 4.0 Scheduling problems
title Knowledge representation in Industry 4.0 Scheduling problems
spellingShingle Knowledge representation in Industry 4.0 Scheduling problems
Rossit, Daniel Alejandro
CYBER-PHYSICAL SYSTEMS
INDUSTRY 4.0
SCHEDULING
DECISIONAL DNA
KNOWLEDGE REPRESENTATION
title_short Knowledge representation in Industry 4.0 Scheduling problems
title_full Knowledge representation in Industry 4.0 Scheduling problems
title_fullStr Knowledge representation in Industry 4.0 Scheduling problems
title_full_unstemmed Knowledge representation in Industry 4.0 Scheduling problems
title_sort Knowledge representation in Industry 4.0 Scheduling problems
dc.creator.none.fl_str_mv Rossit, Daniel Alejandro
Tohmé, Fernando Abel
author Rossit, Daniel Alejandro
author_facet Rossit, Daniel Alejandro
Tohmé, Fernando Abel
author_role author
author2 Tohmé, Fernando Abel
author2_role author
dc.subject.none.fl_str_mv CYBER-PHYSICAL SYSTEMS
INDUSTRY 4.0
SCHEDULING
DECISIONAL DNA
KNOWLEDGE REPRESENTATION
topic CYBER-PHYSICAL SYSTEMS
INDUSTRY 4.0
SCHEDULING
DECISIONAL DNA
KNOWLEDGE REPRESENTATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Industry 4.0 raises the need for a closer integration of management systems in manufacturing companies. Such process is driven by Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Starting from the potential of these technologies, a knowledge architecture aimed at addressing scheduling problems is proposed. Scheduling-support systems generally do not solve real-world scheduling problems, being instead only capable of solving simplified versions, producing solutions that human schedulers adapt to real problems. The architecture aims to record and consolidate the empirical knowledge generated by the solutions of actual scheduling problems. In this way, it summarizes the implicit criteria used by human schedulers. The architecture presented here records this knowledge in data structures compatible with the structure of scheduling problems. In further iterations this knowledge crystallizes into a sound and smart structure.
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina
description Industry 4.0 raises the need for a closer integration of management systems in manufacturing companies. Such process is driven by Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Starting from the potential of these technologies, a knowledge architecture aimed at addressing scheduling problems is proposed. Scheduling-support systems generally do not solve real-world scheduling problems, being instead only capable of solving simplified versions, producing solutions that human schedulers adapt to real problems. The architecture aims to record and consolidate the empirical knowledge generated by the solutions of actual scheduling problems. In this way, it summarizes the implicit criteria used by human schedulers. The architecture presented here records this knowledge in data structures compatible with the structure of scheduling problems. In further iterations this knowledge crystallizes into a sound and smart structure.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-10
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/11336/165422
Rossit, Daniel Alejandro; Tohmé, Fernando Abel; Knowledge representation in Industry 4.0 Scheduling problems; Taylor & Francis Ltd; International Journal Of Computer Integrated Manufacturing; 2022; 10-1-2022; 5-25
0951-192X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/165422
identifier_str_mv Rossit, Daniel Alejandro; Tohmé, Fernando Abel; Knowledge representation in Industry 4.0 Scheduling problems; Taylor & Francis Ltd; International Journal Of Computer Integrated Manufacturing; 2022; 10-1-2022; 5-25
0951-192X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/action/journalInformation?journalCode=tcim20
info:eu-repo/semantics/altIdentifier/url/https://doi.org/10.1080/0951192X.2021.2022760
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
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis Ltd
publisher.none.fl_str_mv Taylor & Francis Ltd
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 13.22299

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