Scheduling in additive manufacturing problems

Autores
Rodriguez, Jeanette; Rossit, Daniel Alejandro
Año de publicación
2022
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Scheduling problems in additive manufacturing is a problem that can involve considerably morecomplexity than single-stage scheduling problems, since machines can process more than one partwith different geometries simultaneously [1]. To achieve efficiency in terms of the used capacity of themachine, it is necessary to group as many parts as possible in a single job. Since the use of themachines in terms of time depends on the job being processed, how parts are grouped within eachjob comes critical. This implies that the resolution of the nesting problem will have a direct impact onthe objective function of the jobs Schedule. In this work, the objective function to be minimized is theTotal Completion time, wich is obtained by the sum of the completion time of each job. The biggestdifficulty is that the problem is NP-Hard [2], so a purely mathematical approach is insufficient. For thisreason, a hybrid method is proposed that allows linking the benefits of an approach based onmathematical programming but enhanced by heuristic methods. In this way, heuristics are developedthat address the nesting problem incorporating knowledge about the nature of the problem, such asthe influence of the parameters “height” and volume” of the parts in the definition of the Jobs; and thestructure of its solutions. Then, using mathematical programming, solve the scheduling in paralleladditive manufacturing machines. For the nesting stage, several heuristics were proposed andcompared, showing that those heuristics that best captured the influence of the parameterscontributed more to solving the problem.
Fil: Rodriguez, Jeanette. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
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
XXI Latin Ibero-American Conference on Operations Research CLAIO 2022
Buenos Aires
Argentina
Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales
Materia
SCHEDULING
TOTAL COMPLETION TIME
HEURISTIC
PARALLEL MACHINE PROBLEM
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:11336/197919
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Scheduling in additive manufacturing problemsRodriguez, JeanetteRossit, Daniel AlejandroSCHEDULINGTOTAL COMPLETION TIMEHEURISTICPARALLEL MACHINE PROBLEMhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Scheduling problems in additive manufacturing is a problem that can involve considerably morecomplexity than single-stage scheduling problems, since machines can process more than one partwith different geometries simultaneously [1]. To achieve efficiency in terms of the used capacity of themachine, it is necessary to group as many parts as possible in a single job. Since the use of themachines in terms of time depends on the job being processed, how parts are grouped within eachjob comes critical. This implies that the resolution of the nesting problem will have a direct impact onthe objective function of the jobs Schedule. In this work, the objective function to be minimized is theTotal Completion time, wich is obtained by the sum of the completion time of each job. The biggestdifficulty is that the problem is NP-Hard [2], so a purely mathematical approach is insufficient. For thisreason, a hybrid method is proposed that allows linking the benefits of an approach based onmathematical programming but enhanced by heuristic methods. In this way, heuristics are developedthat address the nesting problem incorporating knowledge about the nature of the problem, such asthe influence of the parameters “height” and volume” of the parts in the definition of the Jobs; and thestructure of its solutions. Then, using mathematical programming, solve the scheduling in paralleladditive manufacturing machines. For the nesting stage, several heuristics were proposed andcompared, showing that those heuristics that best captured the influence of the parameterscontributed more to solving the problem.Fil: Rodriguez, Jeanette. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: 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; ArgentinaXXI Latin Ibero-American Conference on Operations Research CLAIO 2022Buenos AiresArgentinaUniversidad de Buenos Aires. Facultad de Ciencias Exactas y NaturalesUniversidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoBookhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/197919Scheduling in additive manufacturing problems; XXI Latin Ibero-American Conference on Operations Research CLAIO 2022; Buenos Aires; Argentina; 2022; 138-138CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://claio2022.dc.uba.ar/docs/abstract-book.pdfInternacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:15:58Zoai:ri.conicet.gov.ar:11336/197919instacron: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:34982025-09-10 13:15:58.602CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Scheduling in additive manufacturing problems
title Scheduling in additive manufacturing problems
spellingShingle Scheduling in additive manufacturing problems
Rodriguez, Jeanette
SCHEDULING
TOTAL COMPLETION TIME
HEURISTIC
PARALLEL MACHINE PROBLEM
title_short Scheduling in additive manufacturing problems
title_full Scheduling in additive manufacturing problems
title_fullStr Scheduling in additive manufacturing problems
title_full_unstemmed Scheduling in additive manufacturing problems
title_sort Scheduling in additive manufacturing problems
dc.creator.none.fl_str_mv Rodriguez, Jeanette
Rossit, Daniel Alejandro
author Rodriguez, Jeanette
author_facet Rodriguez, Jeanette
Rossit, Daniel Alejandro
author_role author
author2 Rossit, Daniel Alejandro
author2_role author
dc.subject.none.fl_str_mv SCHEDULING
TOTAL COMPLETION TIME
HEURISTIC
PARALLEL MACHINE PROBLEM
topic SCHEDULING
TOTAL COMPLETION TIME
HEURISTIC
PARALLEL MACHINE PROBLEM
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Scheduling problems in additive manufacturing is a problem that can involve considerably morecomplexity than single-stage scheduling problems, since machines can process more than one partwith different geometries simultaneously [1]. To achieve efficiency in terms of the used capacity of themachine, it is necessary to group as many parts as possible in a single job. Since the use of themachines in terms of time depends on the job being processed, how parts are grouped within eachjob comes critical. This implies that the resolution of the nesting problem will have a direct impact onthe objective function of the jobs Schedule. In this work, the objective function to be minimized is theTotal Completion time, wich is obtained by the sum of the completion time of each job. The biggestdifficulty is that the problem is NP-Hard [2], so a purely mathematical approach is insufficient. For thisreason, a hybrid method is proposed that allows linking the benefits of an approach based onmathematical programming but enhanced by heuristic methods. In this way, heuristics are developedthat address the nesting problem incorporating knowledge about the nature of the problem, such asthe influence of the parameters “height” and volume” of the parts in the definition of the Jobs; and thestructure of its solutions. Then, using mathematical programming, solve the scheduling in paralleladditive manufacturing machines. For the nesting stage, several heuristics were proposed andcompared, showing that those heuristics that best captured the influence of the parameterscontributed more to solving the problem.
Fil: Rodriguez, Jeanette. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
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
XXI Latin Ibero-American Conference on Operations Research CLAIO 2022
Buenos Aires
Argentina
Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales
description Scheduling problems in additive manufacturing is a problem that can involve considerably morecomplexity than single-stage scheduling problems, since machines can process more than one partwith different geometries simultaneously [1]. To achieve efficiency in terms of the used capacity of themachine, it is necessary to group as many parts as possible in a single job. Since the use of themachines in terms of time depends on the job being processed, how parts are grouped within eachjob comes critical. This implies that the resolution of the nesting problem will have a direct impact onthe objective function of the jobs Schedule. In this work, the objective function to be minimized is theTotal Completion time, wich is obtained by the sum of the completion time of each job. The biggestdifficulty is that the problem is NP-Hard [2], so a purely mathematical approach is insufficient. For thisreason, a hybrid method is proposed that allows linking the benefits of an approach based onmathematical programming but enhanced by heuristic methods. In this way, heuristics are developedthat address the nesting problem incorporating knowledge about the nature of the problem, such asthe influence of the parameters “height” and volume” of the parts in the definition of the Jobs; and thestructure of its solutions. Then, using mathematical programming, solve the scheduling in paralleladditive manufacturing machines. For the nesting stage, several heuristics were proposed andcompared, showing that those heuristics that best captured the influence of the parameterscontributed more to solving the problem.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Book
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/197919
Scheduling in additive manufacturing problems; XXI Latin Ibero-American Conference on Operations Research CLAIO 2022; Buenos Aires; Argentina; 2022; 138-138
CONICET Digital
CONICET
url http://hdl.handle.net/11336/197919
identifier_str_mv Scheduling in additive manufacturing problems; XXI Latin Ibero-American Conference on Operations Research CLAIO 2022; Buenos Aires; Argentina; 2022; 138-138
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://claio2022.dc.uba.ar/docs/abstract-book.pdf
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.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales
publisher.none.fl_str_mv Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales
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 12.993085

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