Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product

Autores
Marchetti, Pablo Andres; Mendez, Carlos Alberto; Cerda, Jaime
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A pair of precedence-based continuous-time formulations addressing the combined lot sizing and scheduling of order-driven multistage batch facilities is presented. The proposed mixed-integer linear programming (MILP) models can handle multiple orders per product with different delivery dates, variable processing times, and sequence-dependent changeovers. As each order may be filled by one or more batches, enough batches for each order ensuring optimality are initially defined. The two monolithic formulations are intended for sequential batch processes where batch integrity is preserved throughout the entire production system. However, lots of final products can be split to satisfy two or more orders. One of the approaches is based on a detailed MILP formulation allocating individual batches to units and ordering them in every unit. In contrast, the second methodology is specially designed for large scheduling problems. It first gathers batches for the same order into clusters, and then assigns clusters to units and sequences groups of batches in every unit. The larger the number of groups, the more rigorous is the cluster-based formulation. Alternative sequencing constraints based on reliable assumptions were also tested. Several examples involving up to 92 batches have been successfully solved using one or both formulations.
Fil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Cerda, Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Materia
Batch Processes
Scheduling
Lot-Sizing
Optimization
Integer Programming
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/10897
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/10897
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per productMarchetti, Pablo AndresMendez, Carlos AlbertoCerda, JaimeBatch ProcessesSchedulingLot-SizingOptimizationInteger Programminghttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2A pair of precedence-based continuous-time formulations addressing the combined lot sizing and scheduling of order-driven multistage batch facilities is presented. The proposed mixed-integer linear programming (MILP) models can handle multiple orders per product with different delivery dates, variable processing times, and sequence-dependent changeovers. As each order may be filled by one or more batches, enough batches for each order ensuring optimality are initially defined. The two monolithic formulations are intended for sequential batch processes where batch integrity is preserved throughout the entire production system. However, lots of final products can be split to satisfy two or more orders. One of the approaches is based on a detailed MILP formulation allocating individual batches to units and ordering them in every unit. In contrast, the second methodology is specially designed for large scheduling problems. It first gathers batches for the same order into clusters, and then assigns clusters to units and sequences groups of batches in every unit. The larger the number of groups, the more rigorous is the cluster-based formulation. Alternative sequencing constraints based on reliable assumptions were also tested. Several examples involving up to 92 batches have been successfully solved using one or both formulations.Fil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Cerda, Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaAmerican Chemical Society2012-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/10897Marchetti, Pablo Andres; Mendez, Carlos Alberto; Cerda, Jaime; Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product; American Chemical Society; Industrial & Engineering Chemical Research; 51; 16; 3-2012; 5762-57800888-5885enginfo:eu-repo/semantics/altIdentifier/doi/10.1021/ie202275yinfo:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie202275yinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:21Zoai:ri.conicet.gov.ar:11336/10897instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:47:21.878CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product
title Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product
spellingShingle Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product
Marchetti, Pablo Andres
Batch Processes
Scheduling
Lot-Sizing
Optimization
Integer Programming
title_short Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product
title_full Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product
title_fullStr Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product
title_full_unstemmed Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product
title_sort Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product
dc.creator.none.fl_str_mv Marchetti, Pablo Andres
Mendez, Carlos Alberto
Cerda, Jaime
author Marchetti, Pablo Andres
author_facet Marchetti, Pablo Andres
Mendez, Carlos Alberto
Cerda, Jaime
author_role author
author2 Mendez, Carlos Alberto
Cerda, Jaime
author2_role author
author
dc.subject.none.fl_str_mv Batch Processes
Scheduling
Lot-Sizing
Optimization
Integer Programming
topic Batch Processes
Scheduling
Lot-Sizing
Optimization
Integer Programming
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv A pair of precedence-based continuous-time formulations addressing the combined lot sizing and scheduling of order-driven multistage batch facilities is presented. The proposed mixed-integer linear programming (MILP) models can handle multiple orders per product with different delivery dates, variable processing times, and sequence-dependent changeovers. As each order may be filled by one or more batches, enough batches for each order ensuring optimality are initially defined. The two monolithic formulations are intended for sequential batch processes where batch integrity is preserved throughout the entire production system. However, lots of final products can be split to satisfy two or more orders. One of the approaches is based on a detailed MILP formulation allocating individual batches to units and ordering them in every unit. In contrast, the second methodology is specially designed for large scheduling problems. It first gathers batches for the same order into clusters, and then assigns clusters to units and sequences groups of batches in every unit. The larger the number of groups, the more rigorous is the cluster-based formulation. Alternative sequencing constraints based on reliable assumptions were also tested. Several examples involving up to 92 batches have been successfully solved using one or both formulations.
Fil: Marchetti, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Cerda, Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
description A pair of precedence-based continuous-time formulations addressing the combined lot sizing and scheduling of order-driven multistage batch facilities is presented. The proposed mixed-integer linear programming (MILP) models can handle multiple orders per product with different delivery dates, variable processing times, and sequence-dependent changeovers. As each order may be filled by one or more batches, enough batches for each order ensuring optimality are initially defined. The two monolithic formulations are intended for sequential batch processes where batch integrity is preserved throughout the entire production system. However, lots of final products can be split to satisfy two or more orders. One of the approaches is based on a detailed MILP formulation allocating individual batches to units and ordering them in every unit. In contrast, the second methodology is specially designed for large scheduling problems. It first gathers batches for the same order into clusters, and then assigns clusters to units and sequences groups of batches in every unit. The larger the number of groups, the more rigorous is the cluster-based formulation. Alternative sequencing constraints based on reliable assumptions were also tested. Several examples involving up to 92 batches have been successfully solved using one or both formulations.
publishDate 2012
dc.date.none.fl_str_mv 2012-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/10897
Marchetti, Pablo Andres; Mendez, Carlos Alberto; Cerda, Jaime; Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product; American Chemical Society; Industrial & Engineering Chemical Research; 51; 16; 3-2012; 5762-5780
0888-5885
url http://hdl.handle.net/11336/10897
identifier_str_mv Marchetti, Pablo Andres; Mendez, Carlos Alberto; Cerda, Jaime; Simultaneous lot sizing and scheduling of multistage batch processes handling multiple orders per product; American Chemical Society; Industrial & Engineering Chemical Research; 51; 16; 3-2012; 5762-5780
0888-5885
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1021/ie202275y
info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie202275y
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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