Nonlinear cutting stock problem model to minimize the number of different patterns and objects
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Computational & Applied Mathematics |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022008000100004 |
Resumo: | In this article we solve a nonlinear cutting stock problem which represents a cutting stock problem that considers the minimization of, both, the number of objects used and setup. We use a linearization of the nonlinear objective function to make possible the generation of good columns with the Gilmore and Gomory procedure. Each time a new column is added to the problem, we solve the original nonlinear problem by an Augmented Lagrangian method. This process is repeated until no more profitable columns is generated by Gilmore and Gomory technique. Finally, we apply a simple heuristic to obtain an integral solution for the original nonlinear integer problem. |
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Computational & Applied Mathematics |
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Nonlinear cutting stock problem model to minimize the number of different patterns and objectscutting problemnonlinear programmingcolumn generationIn this article we solve a nonlinear cutting stock problem which represents a cutting stock problem that considers the minimization of, both, the number of objects used and setup. We use a linearization of the nonlinear objective function to make possible the generation of good columns with the Gilmore and Gomory procedure. Each time a new column is added to the problem, we solve the original nonlinear problem by an Augmented Lagrangian method. This process is repeated until no more profitable columns is generated by Gilmore and Gomory technique. Finally, we apply a simple heuristic to obtain an integral solution for the original nonlinear integer problem.Sociedade Brasileira de Matemática Aplicada e Computacional2008-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022008000100004Computational & Applied Mathematics v.27 n.1 2008reponame:Computational & Applied Mathematicsinstname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)instacron:SBMACinfo:eu-repo/semantics/openAccessMoretti,Antonio CarlosSalles Neto,Luiz Leduíno deeng2008-04-02T00:00:00Zoai:scielo:S1807-03022008000100004Revistahttps://www.scielo.br/j/cam/ONGhttps://old.scielo.br/oai/scielo-oai.php||sbmac@sbmac.org.br1807-03022238-3603opendoar:2008-04-02T00:00Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC)false |
dc.title.none.fl_str_mv |
Nonlinear cutting stock problem model to minimize the number of different patterns and objects |
title |
Nonlinear cutting stock problem model to minimize the number of different patterns and objects |
spellingShingle |
Nonlinear cutting stock problem model to minimize the number of different patterns and objects Moretti,Antonio Carlos cutting problem nonlinear programming column generation |
title_short |
Nonlinear cutting stock problem model to minimize the number of different patterns and objects |
title_full |
Nonlinear cutting stock problem model to minimize the number of different patterns and objects |
title_fullStr |
Nonlinear cutting stock problem model to minimize the number of different patterns and objects |
title_full_unstemmed |
Nonlinear cutting stock problem model to minimize the number of different patterns and objects |
title_sort |
Nonlinear cutting stock problem model to minimize the number of different patterns and objects |
author |
Moretti,Antonio Carlos |
author_facet |
Moretti,Antonio Carlos Salles Neto,Luiz Leduíno de |
author_role |
author |
author2 |
Salles Neto,Luiz Leduíno de |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Moretti,Antonio Carlos Salles Neto,Luiz Leduíno de |
dc.subject.por.fl_str_mv |
cutting problem nonlinear programming column generation |
topic |
cutting problem nonlinear programming column generation |
description |
In this article we solve a nonlinear cutting stock problem which represents a cutting stock problem that considers the minimization of, both, the number of objects used and setup. We use a linearization of the nonlinear objective function to make possible the generation of good columns with the Gilmore and Gomory procedure. Each time a new column is added to the problem, we solve the original nonlinear problem by an Augmented Lagrangian method. This process is repeated until no more profitable columns is generated by Gilmore and Gomory technique. Finally, we apply a simple heuristic to obtain an integral solution for the original nonlinear integer problem. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022008000100004 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-03022008000100004 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Matemática Aplicada e Computacional |
publisher.none.fl_str_mv |
Sociedade Brasileira de Matemática Aplicada e Computacional |
dc.source.none.fl_str_mv |
Computational & Applied Mathematics v.27 n.1 2008 reponame:Computational & Applied Mathematics instname:Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC) instacron:SBMAC |
instname_str |
Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC) |
instacron_str |
SBMAC |
institution |
SBMAC |
reponame_str |
Computational & Applied Mathematics |
collection |
Computational & Applied Mathematics |
repository.name.fl_str_mv |
Computational & Applied Mathematics - Sociedade Brasileira de Matemática Aplicada e Computacional (SBMAC) |
repository.mail.fl_str_mv |
||sbmac@sbmac.org.br |
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1754734890156294144 |