Materials requirements planning through an application based on particle swarm optimization

This work approaches the Material Requirements Planning (MRP) problem through a Particle Swarm Optimization (PSO) based algorithm. Results from various computer experiments are carried out considering different production capacity levels, where the algorithm shown good performance. It can be observe...

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Autores:
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/8964
Acceso en línea:
https://hdl.handle.net/20.500.12585/8964
Palabra clave:
Lot sizing
Material requirement planning (MRP)
Particle swarm optimization
Production planning
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restrictedAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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spelling 2020-03-26T16:32:40Z2020-03-26T16:32:40Z2017Espacios; Vol. 38, Núm. 140798-1015https://hdl.handle.net/20.500.12585/8964Universidad Tecnológica de BolívarRepositorio UTB5719374774354383095000557824265005719350289957193746869This work approaches the Material Requirements Planning (MRP) problem through a Particle Swarm Optimization (PSO) based algorithm. Results from various computer experiments are carried out considering different production capacity levels, where the algorithm shown good performance. It can be observed that the solutions found by the algorithm surpass by 6.31% the solution presented in the reviewed literature. © 2017.Recurso electrónicoapplication/pdfengRevista Espacioshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85016298121&partnerID=40&md5=00c7b7af9570f71654b856bd0017dd61Materials requirements planning through an application based on particle swarm optimizationPlanificación de requerimientos de materiales por medio de la aplicación de un algoritmo basado en enjambre de partículasinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Lot sizingMaterial requirement planning (MRP)Particle swarm optimizationProduction planningPadron-Cano J.Coronado Hernández, Jairo RafaelCaicedo-Torres W.Mercado-Caruso N.Ospino-Vadiris F.V.Billington, P., Mcmclain, J., Thomas, J., Mathematical programming approaches to capacity-constrained MRP systems: review, formulation and problem reduction (1983) Management Science, 29 (10), pp. 1126-1141. , http://pubsonline.informs.org/doi/abs/10.1287/mnsc.29.10.1126Florian, M., Lenstra, J., Rinooy, K., Deterministic production planning: algorithms and complexity (1980) Management Science, 26, pp. 669-679Han, Y., Tang, J., Kaku, I., Mu, L., Solving uncapacitated multilevel lot-sizing problems using a particle swarm optimization with flexible inertial weight (2009) Computers and Mathematics with Applications, 57 (11-12), pp. 1748-1755Herzer, M., Menezes, F.M., Possebon, A.P., Nunes, F.L., Avaliação da utilização de metodologias ativas no ensino superior: estudo de caso na disciplina de gestão da produção aplicada (2015) ESPACIOS, 37 (2). , http://www.revistaespacios.com/a16v37n02/163702e3.htmlKennedy, J., Eberhart, R., Particle swarm optimizatio (1995) Neural Networks, 1995. Proceedings., IEEE International Conference on, 4, pp. 1942-1948Moreira, E., Pacagnella, A.C., Pacífico, O., Salgado, A.P., Contribuições do planejamento e controle da produção para a competitividade empresarial: um estudo em uma empresa do setor moveleiro (2014) Espacios, 35 (9), p. 5. , http://www.revistaespacios.com/a14v35n09/14350905.htmlOrlicky, J., (1975) Material Requirements Planning, , United States of America: McGraw -HillSana, S.S., Acevedo, J., Salas, K., A three layer supply chain model with multiple suppliers, manufacturers and retailers for multiple items (2014) Applied Mathematics and Computation, 229, pp. 139-150Wajanawichakon, K., Pitakaso, R., (2013) Solving large unconstrainted multi level lot-sizing problem by a binary particle swarm optimization, , International Journal of Management Science and Engineering Managementhttp://purl.org/coar/resource_type/c_6501THUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8964/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8964oai:repositorio.utb.edu.co:20.500.12585/89642023-05-25 16:16:23.179Repositorio Institucional UTBrepositorioutb@utb.edu.co
dc.title.none.fl_str_mv Materials requirements planning through an application based on particle swarm optimization
dc.title.alternative.none.fl_str_mv Planificación de requerimientos de materiales por medio de la aplicación de un algoritmo basado en enjambre de partículas
title Materials requirements planning through an application based on particle swarm optimization
spellingShingle Materials requirements planning through an application based on particle swarm optimization
Lot sizing
Material requirement planning (MRP)
Particle swarm optimization
Production planning
title_short Materials requirements planning through an application based on particle swarm optimization
title_full Materials requirements planning through an application based on particle swarm optimization
title_fullStr Materials requirements planning through an application based on particle swarm optimization
title_full_unstemmed Materials requirements planning through an application based on particle swarm optimization
title_sort Materials requirements planning through an application based on particle swarm optimization
dc.subject.keywords.none.fl_str_mv Lot sizing
Material requirement planning (MRP)
Particle swarm optimization
Production planning
topic Lot sizing
Material requirement planning (MRP)
Particle swarm optimization
Production planning
description This work approaches the Material Requirements Planning (MRP) problem through a Particle Swarm Optimization (PSO) based algorithm. Results from various computer experiments are carried out considering different production capacity levels, where the algorithm shown good performance. It can be observed that the solutions found by the algorithm surpass by 6.31% the solution presented in the reviewed literature. © 2017.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2020-03-26T16:32:40Z
dc.date.available.none.fl_str_mv 2020-03-26T16:32:40Z
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dc.type.spa.none.fl_str_mv Artículo
status_str publishedVersion
dc.identifier.citation.none.fl_str_mv Espacios; Vol. 38, Núm. 14
dc.identifier.issn.none.fl_str_mv 0798-1015
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/8964
dc.identifier.instname.none.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.none.fl_str_mv Repositorio UTB
dc.identifier.orcid.none.fl_str_mv 57193747743
54383095000
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identifier_str_mv Espacios; Vol. 38, Núm. 14
0798-1015
Universidad Tecnológica de Bolívar
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57193746869
url https://hdl.handle.net/20.500.12585/8964
dc.language.iso.none.fl_str_mv eng
language eng
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