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dc.contributor.author
Rodriguez, Jeanette
dc.contributor.author
Rossit, Daniel Alejandro
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dc.date.available
2023-05-18T11:20:53Z
dc.date.issued
2022
dc.identifier.citation
Scheduling in additive manufacturing problems; XXI Latin Ibero-American Conference on Operations Research CLAIO 2022; Buenos Aires; Argentina; 2022; 138-138
dc.identifier.uri
http://hdl.handle.net/11336/197919
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
SCHEDULING
dc.subject
TOTAL COMPLETION TIME
dc.subject
HEURISTIC
dc.subject
PARALLEL MACHINE PROBLEM
dc.subject.classification
Otras Ingenierías y Tecnologías
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dc.subject.classification
Otras Ingenierías y Tecnologías
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Scheduling in additive manufacturing problems
dc.type
info:eu-repo/semantics/publishedVersion
dc.type
info:eu-repo/semantics/conferenceObject
dc.type
info:ar-repo/semantics/documento de conferencia
dc.date.updated
2023-05-10T15:31:44Z
dc.journal.pagination
138-138
dc.journal.pais
Argentina
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dc.journal.ciudad
Buenos Aires
dc.description.fil
Fil: Rodriguez, Jeanette. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
dc.description.fil
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
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://claio2022.dc.uba.ar/docs/abstract-book.pdf
dc.conicet.rol
Autor
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dc.conicet.rol
Autor
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dc.coverage
Internacional
dc.type.subtype
Congreso
dc.description.nombreEvento
XXI Latin Ibero-American Conference on Operations Research CLAIO 2022
dc.date.evento
2022-12-12
dc.description.ciudadEvento
Buenos Aires
dc.description.paisEvento
Argentina
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dc.type.publicacion
Book
dc.description.institucionOrganizadora
Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales
dc.source.libro
Abstract Book: XXI Latin Ibero-American Conference on Operations Research. CLAIO 2022
dc.date.eventoHasta
2022-12-15
dc.type
Congreso
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