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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/18787
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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 Técnicas2025-09-03T09:55:25Zoai: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:34982025-09-03 09:55:26.021CONICET 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) |
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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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1842269343041191936 |
score |
13.13397 |