A Genetic Algorithm applied to pick sequencing for billing
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s10845-015-1116-7 http://hdl.handle.net/11449/163832 |
Resumo: | This article addresses the use of Holland's Genetic Algorithms (GAs) (Holland in Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI, 1975) in solving an optimization problem not exploited yet by literature, which we have named Optimal Billing Sequencing (OBS). The objective of the GA proposed is to automate pick sequencing, which addresses the process of allocating the stock available for sale to the purchase orders in a portfolio, so that the maximization of the billing is the optimal result for the OBS. A modelling and computational simulation methodology has been employed. Such methodology is designed to enable the GA to meet the boundary conditions established by predefined decision restrictions and parameters. We have reached the conclusion, by means of experimental tests, that the GA developed satisfactorily solves the problem studied. In addition to a low computational overhead, the GA reduces operating costs and speeds picking decision-making processes and billing processes. |
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A Genetic Algorithm applied to pick sequencing for billingGenetic AlgorithmsPicking processBilling sequencingThis article addresses the use of Holland's Genetic Algorithms (GAs) (Holland in Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI, 1975) in solving an optimization problem not exploited yet by literature, which we have named Optimal Billing Sequencing (OBS). The objective of the GA proposed is to automate pick sequencing, which addresses the process of allocating the stock available for sale to the purchase orders in a portfolio, so that the maximization of the billing is the optimal result for the OBS. A modelling and computational simulation methodology has been employed. Such methodology is designed to enable the GA to meet the boundary conditions established by predefined decision restrictions and parameters. We have reached the conclusion, by means of experimental tests, that the GA developed satisfactorily solves the problem studied. In addition to a low computational overhead, the GA reduces operating costs and speeds picking decision-making processes and billing processes.Univ Sao Paulo, Sch Engn Sao Carlos, Dept Prod Engn, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP, BrazilSao Paulo State Univ, Fac Engn Bauru, Dept Prod Engn, Ave Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, BrazilSao Paulo State Univ, Fac Engn Bauru, Dept Prod Engn, Ave Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, BrazilSpringerUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Faia Pinto, Anderson RogerioCrepaldi, Antonio Fernando [UNESP]Nagano, Marcelo Seido2018-11-26T17:45:08Z2018-11-26T17:45:08Z2018-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article405-422application/pdfhttp://dx.doi.org/10.1007/s10845-015-1116-7Journal Of Intelligent Manufacturing. Dordrecht: Springer, v. 29, n. 2, p. 405-422, 2018.0956-5515http://hdl.handle.net/11449/16383210.1007/s10845-015-1116-7WOS:000424642800009WOS000424642800009.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal Of Intelligent Manufacturing1,179info:eu-repo/semantics/openAccess2024-06-28T13:18:21Zoai:repositorio.unesp.br:11449/163832Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:36:15.904671Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Genetic Algorithm applied to pick sequencing for billing |
title |
A Genetic Algorithm applied to pick sequencing for billing |
spellingShingle |
A Genetic Algorithm applied to pick sequencing for billing Faia Pinto, Anderson Rogerio Genetic Algorithms Picking process Billing sequencing |
title_short |
A Genetic Algorithm applied to pick sequencing for billing |
title_full |
A Genetic Algorithm applied to pick sequencing for billing |
title_fullStr |
A Genetic Algorithm applied to pick sequencing for billing |
title_full_unstemmed |
A Genetic Algorithm applied to pick sequencing for billing |
title_sort |
A Genetic Algorithm applied to pick sequencing for billing |
author |
Faia Pinto, Anderson Rogerio |
author_facet |
Faia Pinto, Anderson Rogerio Crepaldi, Antonio Fernando [UNESP] Nagano, Marcelo Seido |
author_role |
author |
author2 |
Crepaldi, Antonio Fernando [UNESP] Nagano, Marcelo Seido |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Faia Pinto, Anderson Rogerio Crepaldi, Antonio Fernando [UNESP] Nagano, Marcelo Seido |
dc.subject.por.fl_str_mv |
Genetic Algorithms Picking process Billing sequencing |
topic |
Genetic Algorithms Picking process Billing sequencing |
description |
This article addresses the use of Holland's Genetic Algorithms (GAs) (Holland in Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI, 1975) in solving an optimization problem not exploited yet by literature, which we have named Optimal Billing Sequencing (OBS). The objective of the GA proposed is to automate pick sequencing, which addresses the process of allocating the stock available for sale to the purchase orders in a portfolio, so that the maximization of the billing is the optimal result for the OBS. A modelling and computational simulation methodology has been employed. Such methodology is designed to enable the GA to meet the boundary conditions established by predefined decision restrictions and parameters. We have reached the conclusion, by means of experimental tests, that the GA developed satisfactorily solves the problem studied. In addition to a low computational overhead, the GA reduces operating costs and speeds picking decision-making processes and billing processes. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-11-26T17:45:08Z 2018-11-26T17:45:08Z 2018-02-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s10845-015-1116-7 Journal Of Intelligent Manufacturing. Dordrecht: Springer, v. 29, n. 2, p. 405-422, 2018. 0956-5515 http://hdl.handle.net/11449/163832 10.1007/s10845-015-1116-7 WOS:000424642800009 WOS000424642800009.pdf |
url |
http://dx.doi.org/10.1007/s10845-015-1116-7 http://hdl.handle.net/11449/163832 |
identifier_str_mv |
Journal Of Intelligent Manufacturing. Dordrecht: Springer, v. 29, n. 2, p. 405-422, 2018. 0956-5515 10.1007/s10845-015-1116-7 WOS:000424642800009 WOS000424642800009.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal Of Intelligent Manufacturing 1,179 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
405-422 application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129225229598720 |