An improvement-based MILP optimization approach to complex AWS scheduling

Autores
Aguirre, Adrian Marcelo; Mendez, Carlos Alberto; Gutierrez, Gloria Maribel; de Prada, Cesar
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The automated wet-etch station (AWS) is one of the most critical stages of a modern semiconductor manufacturing system (SMS), which has to simultaneously deal with many complex constraints and limited resources. Due to its inherent complexity, industrial-sized automated wet-etch station scheduling problems are rarely solved through full rigorous mathematical formulations. Decomposition techniques based on heuristic, meta-heuristics and simulation-based methods have been traditionally reported in literature to provide feasible solutions with reasonable CPU times. This work introduces an improvement MILP-based decomposition strategy that combines the benefits of a rigorous continuous-time MILP (mixed integer linear programming) formulation with the flexibility of heuristic procedures. The schedule generated provides enhanced solutions over time to challenging real-world automated wet etch station scheduling problems with moderate computational cost. This methodology was able to provide more than a 7% of improvement in comparison with the best results reported in literature for the most complex problem instances analyzed.
Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico Para la Industria Química; Argentina
Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Gutierrez, Gloria Maribel. Universidad de Valladolid; España
Fil: de Prada, Cesar. Universidad de Valladolid; España
Materia
Hybrid Decomposition Approach
Milp-Based Strategies
Large-Scale Scheduling Problems
Semiconductor Manufacturing System (Sms)
Wafer Fabrication
Modeling And Optimization
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:11336/18787
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/18787
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An improvement-based MILP optimization approach to complex AWS schedulingAguirre, Adrian MarceloMendez, Carlos AlbertoGutierrez, Gloria Maribelde Prada, CesarHybrid Decomposition ApproachMilp-Based StrategiesLarge-Scale Scheduling ProblemsSemiconductor Manufacturing System (Sms)Wafer FabricationModeling And Optimizationhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2The automated wet-etch station (AWS) is one of the most critical stages of a modern semiconductor manufacturing system (SMS), which has to simultaneously deal with many complex constraints and limited resources. Due to its inherent complexity, industrial-sized automated wet-etch station scheduling problems are rarely solved through full rigorous mathematical formulations. Decomposition techniques based on heuristic, meta-heuristics and simulation-based methods have been traditionally reported in literature to provide feasible solutions with reasonable CPU times. This work introduces an improvement MILP-based decomposition strategy that combines the benefits of a rigorous continuous-time MILP (mixed integer linear programming) formulation with the flexibility of heuristic procedures. The schedule generated provides enhanced solutions over time to challenging real-world automated wet etch station scheduling problems with moderate computational cost. This methodology was able to provide more than a 7% of improvement in comparison with the best results reported in literature for the most complex problem instances analyzed.Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico Para la Industria Química; ArgentinaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Gutierrez, Gloria Maribel. Universidad de Valladolid; EspañaFil: de Prada, Cesar. Universidad de Valladolid; EspañaElsevier2012-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/18787Aguirre, Adrian Marcelo; Mendez, Carlos Alberto; Gutierrez, Gloria Maribel; de Prada, Cesar; An improvement-based MILP optimization approach to complex AWS scheduling; Elsevier; Computers And Chemical Engineering; 47; 12-2012; 217-2260098-1354CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0098135412002207info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compchemeng.2012.06.036info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:57:58Zoai:ri.conicet.gov.ar:11336/18787instacron: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:34982025-09-29 09:57:58.807CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An improvement-based MILP optimization approach to complex AWS scheduling
title An improvement-based MILP optimization approach to complex AWS scheduling
spellingShingle An improvement-based MILP optimization approach to complex AWS scheduling
Aguirre, Adrian Marcelo
Hybrid Decomposition Approach
Milp-Based Strategies
Large-Scale Scheduling Problems
Semiconductor Manufacturing System (Sms)
Wafer Fabrication
Modeling And Optimization
title_short An improvement-based MILP optimization approach to complex AWS scheduling
title_full An improvement-based MILP optimization approach to complex AWS scheduling
title_fullStr An improvement-based MILP optimization approach to complex AWS scheduling
title_full_unstemmed An improvement-based MILP optimization approach to complex AWS scheduling
title_sort An improvement-based MILP optimization approach to complex AWS scheduling
dc.creator.none.fl_str_mv Aguirre, Adrian Marcelo
Mendez, Carlos Alberto
Gutierrez, Gloria Maribel
de Prada, Cesar
author Aguirre, Adrian Marcelo
author_facet Aguirre, Adrian Marcelo
Mendez, Carlos Alberto
Gutierrez, Gloria Maribel
de Prada, Cesar
author_role author
author2 Mendez, Carlos Alberto
Gutierrez, Gloria Maribel
de Prada, Cesar
author2_role author
author
author
dc.subject.none.fl_str_mv Hybrid Decomposition Approach
Milp-Based Strategies
Large-Scale Scheduling Problems
Semiconductor Manufacturing System (Sms)
Wafer Fabrication
Modeling And Optimization
topic Hybrid Decomposition Approach
Milp-Based Strategies
Large-Scale Scheduling Problems
Semiconductor Manufacturing System (Sms)
Wafer Fabrication
Modeling And Optimization
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The automated wet-etch station (AWS) is one of the most critical stages of a modern semiconductor manufacturing system (SMS), which has to simultaneously deal with many complex constraints and limited resources. Due to its inherent complexity, industrial-sized automated wet-etch station scheduling problems are rarely solved through full rigorous mathematical formulations. Decomposition techniques based on heuristic, meta-heuristics and simulation-based methods have been traditionally reported in literature to provide feasible solutions with reasonable CPU times. This work introduces an improvement MILP-based decomposition strategy that combines the benefits of a rigorous continuous-time MILP (mixed integer linear programming) formulation with the flexibility of heuristic procedures. The schedule generated provides enhanced solutions over time to challenging real-world automated wet etch station scheduling problems with moderate computational cost. This methodology was able to provide more than a 7% of improvement in comparison with the best results reported in literature for the most complex problem instances analyzed.
Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico Para la Industria Química; Argentina
Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Gutierrez, Gloria Maribel. Universidad de Valladolid; España
Fil: de Prada, Cesar. Universidad de Valladolid; España
description The automated wet-etch station (AWS) is one of the most critical stages of a modern semiconductor manufacturing system (SMS), which has to simultaneously deal with many complex constraints and limited resources. Due to its inherent complexity, industrial-sized automated wet-etch station scheduling problems are rarely solved through full rigorous mathematical formulations. Decomposition techniques based on heuristic, meta-heuristics and simulation-based methods have been traditionally reported in literature to provide feasible solutions with reasonable CPU times. This work introduces an improvement MILP-based decomposition strategy that combines the benefits of a rigorous continuous-time MILP (mixed integer linear programming) formulation with the flexibility of heuristic procedures. The schedule generated provides enhanced solutions over time to challenging real-world automated wet etch station scheduling problems with moderate computational cost. This methodology was able to provide more than a 7% of improvement in comparison with the best results reported in literature for the most complex problem instances analyzed.
publishDate 2012
dc.date.none.fl_str_mv 2012-12
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/11336/18787
Aguirre, Adrian Marcelo; Mendez, Carlos Alberto; Gutierrez, Gloria Maribel; de Prada, Cesar; An improvement-based MILP optimization approach to complex AWS scheduling; Elsevier; Computers And Chemical Engineering; 47; 12-2012; 217-226
0098-1354
CONICET Digital
CONICET
url http://hdl.handle.net/11336/18787
identifier_str_mv Aguirre, Adrian Marcelo; Mendez, Carlos Alberto; Gutierrez, Gloria Maribel; de Prada, Cesar; An improvement-based MILP optimization approach to complex AWS scheduling; Elsevier; Computers And Chemical Engineering; 47; 12-2012; 217-226
0098-1354
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0098135412002207
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compchemeng.2012.06.036
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
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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 13.069144

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