MATHEMATICAL MODELLING AND SOLUTION APPROACHES FOR PRODUCTION PLANNING IN A CHEMICAL INDUSTRY

Detalhes bibliográficos
Autor(a) principal: Cunha,Artur Lovato
Data de Publicação: 2017
Outros Autores: Santos,Maristela Oliveira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Pesquisa operacional (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000200311
Resumo: ABSTRACT This paper addresses a lot sizing problem in a Brazilian chemical industry where a product can be produced by more than one process, which can use different parallel machines and may even consume a wide range of raw materials. Moreover, most of the products are liquids and the inventories must be kept in a restricted number of storage tanks with a limited capacity. Hence, these two issues are barely addressed in the literature on lot sizing. The classical multi-level capacitated lot sizing problem was extended to address them and a mixed integer programming (MIP) formulation was developed to determine how many batches should be produced and in which tank products should be stored to meet the demands and minimize production costs. The results of computational experiments show that the commercial solver found poor quality solutions or could not find feasible solutions within one hour. Thus, we applied relaxand-fix and fix-and-optimize MIP based heuristics and we observed that these heuristics were able to obtain feasible solutions for more instances in shorter computational times and find better solutions than those obtained by the commercial solver to solve the proposed model.
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spelling MATHEMATICAL MODELLING AND SOLUTION APPROACHES FOR PRODUCTION PLANNING IN A CHEMICAL INDUSTRYLot sizing problemMixed integer programming problemChemical industryRelax-and-fixFix-and-optimizeABSTRACT This paper addresses a lot sizing problem in a Brazilian chemical industry where a product can be produced by more than one process, which can use different parallel machines and may even consume a wide range of raw materials. Moreover, most of the products are liquids and the inventories must be kept in a restricted number of storage tanks with a limited capacity. Hence, these two issues are barely addressed in the literature on lot sizing. The classical multi-level capacitated lot sizing problem was extended to address them and a mixed integer programming (MIP) formulation was developed to determine how many batches should be produced and in which tank products should be stored to meet the demands and minimize production costs. The results of computational experiments show that the commercial solver found poor quality solutions or could not find feasible solutions within one hour. Thus, we applied relaxand-fix and fix-and-optimize MIP based heuristics and we observed that these heuristics were able to obtain feasible solutions for more instances in shorter computational times and find better solutions than those obtained by the commercial solver to solve the proposed model.Sociedade Brasileira de Pesquisa Operacional2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000200311Pesquisa Operacional v.37 n.2 2017reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/0101-7438.2017.037.02.0311info:eu-repo/semantics/openAccessCunha,Artur LovatoSantos,Maristela Oliveiraeng2017-09-22T00:00:00Zoai:scielo:S0101-74382017000200311Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2017-09-22T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false
dc.title.none.fl_str_mv MATHEMATICAL MODELLING AND SOLUTION APPROACHES FOR PRODUCTION PLANNING IN A CHEMICAL INDUSTRY
title MATHEMATICAL MODELLING AND SOLUTION APPROACHES FOR PRODUCTION PLANNING IN A CHEMICAL INDUSTRY
spellingShingle MATHEMATICAL MODELLING AND SOLUTION APPROACHES FOR PRODUCTION PLANNING IN A CHEMICAL INDUSTRY
Cunha,Artur Lovato
Lot sizing problem
Mixed integer programming problem
Chemical industry
Relax-and-fix
Fix-and-optimize
title_short MATHEMATICAL MODELLING AND SOLUTION APPROACHES FOR PRODUCTION PLANNING IN A CHEMICAL INDUSTRY
title_full MATHEMATICAL MODELLING AND SOLUTION APPROACHES FOR PRODUCTION PLANNING IN A CHEMICAL INDUSTRY
title_fullStr MATHEMATICAL MODELLING AND SOLUTION APPROACHES FOR PRODUCTION PLANNING IN A CHEMICAL INDUSTRY
title_full_unstemmed MATHEMATICAL MODELLING AND SOLUTION APPROACHES FOR PRODUCTION PLANNING IN A CHEMICAL INDUSTRY
title_sort MATHEMATICAL MODELLING AND SOLUTION APPROACHES FOR PRODUCTION PLANNING IN A CHEMICAL INDUSTRY
author Cunha,Artur Lovato
author_facet Cunha,Artur Lovato
Santos,Maristela Oliveira
author_role author
author2 Santos,Maristela Oliveira
author2_role author
dc.contributor.author.fl_str_mv Cunha,Artur Lovato
Santos,Maristela Oliveira
dc.subject.por.fl_str_mv Lot sizing problem
Mixed integer programming problem
Chemical industry
Relax-and-fix
Fix-and-optimize
topic Lot sizing problem
Mixed integer programming problem
Chemical industry
Relax-and-fix
Fix-and-optimize
description ABSTRACT This paper addresses a lot sizing problem in a Brazilian chemical industry where a product can be produced by more than one process, which can use different parallel machines and may even consume a wide range of raw materials. Moreover, most of the products are liquids and the inventories must be kept in a restricted number of storage tanks with a limited capacity. Hence, these two issues are barely addressed in the literature on lot sizing. The classical multi-level capacitated lot sizing problem was extended to address them and a mixed integer programming (MIP) formulation was developed to determine how many batches should be produced and in which tank products should be stored to meet the demands and minimize production costs. The results of computational experiments show that the commercial solver found poor quality solutions or could not find feasible solutions within one hour. Thus, we applied relaxand-fix and fix-and-optimize MIP based heuristics and we observed that these heuristics were able to obtain feasible solutions for more instances in shorter computational times and find better solutions than those obtained by the commercial solver to solve the proposed model.
publishDate 2017
dc.date.none.fl_str_mv 2017-08-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000200311
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000200311
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0101-7438.2017.037.02.0311
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 Pesquisa Operacional
publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Operacional
dc.source.none.fl_str_mv Pesquisa Operacional v.37 n.2 2017
reponame:Pesquisa operacional (Online)
instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
instacron:SOBRAPO
instname_str Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
instacron_str SOBRAPO
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reponame_str Pesquisa operacional (Online)
collection Pesquisa operacional (Online)
repository.name.fl_str_mv Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
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