Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm

Autores
Yongheng, Jiang; Rodriguez, Maria Analia; Harjunkoski, Iiro; Grossmann, Ignacio E.
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In Part I (Rodriguez, Vecchietti, Harjunkoski, & Grossmann, 2014), we proposed an optimization model to redesign the supply chain of spare parts industry under demand uncertainty from strategic and tactical perspectives in a planning horizon consisting of multiple time periods. To address large scale industrial problems, a Lagrangean scheme is proposed to decompose the MINLP of Part I according to the warehouses. The subproblems are first approximated by an adaptive piece-wise linearization scheme that provides lower bounds, and the MILP is further relaxed to an LP to improve solution efficiency while providing a valid lower bound. An initialization scheme is designed to obtain good initial Lagrange multipliers, which are scaled to accelerate the convergence. To obtain feasible solutions, an adaptive linearization scheme is also introduced. The results from an illustrative problem and two real world industrial problems show that the method can obtain optimal or near optimal solutions in modest computational times.
Fil: Yongheng, Jiang. Tsinghua University, Institute of Process Control Engineering; China
Fil: Rodriguez, Maria Analia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Harjunkoski, Iiro. ABB Corporate Research; Alemania
Fil: Grossmann, Ignacio E.. University of Carnegie Mellon. Department of Chemical Engineering; Estados Unidos
Materia
Supply Chain
Lagrangean Decomposition
Adaptive Piecewise Linearization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/21765
Acceder
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spelling Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithmYongheng, JiangRodriguez, Maria AnaliaHarjunkoski, IiroGrossmann, Ignacio E.Supply ChainLagrangean DecompositionAdaptive Piecewise LinearizationIn Part I (Rodriguez, Vecchietti, Harjunkoski, & Grossmann, 2014), we proposed an optimization model to redesign the supply chain of spare parts industry under demand uncertainty from strategic and tactical perspectives in a planning horizon consisting of multiple time periods. To address large scale industrial problems, a Lagrangean scheme is proposed to decompose the MINLP of Part I according to the warehouses. The subproblems are first approximated by an adaptive piece-wise linearization scheme that provides lower bounds, and the MILP is further relaxed to an LP to improve solution efficiency while providing a valid lower bound. An initialization scheme is designed to obtain good initial Lagrange multipliers, which are scaled to accelerate the convergence. To obtain feasible solutions, an adaptive linearization scheme is also introduced. The results from an illustrative problem and two real world industrial problems show that the method can obtain optimal or near optimal solutions in modest computational times.Fil: Yongheng, Jiang. Tsinghua University, Institute of Process Control Engineering; ChinaFil: Rodriguez, Maria Analia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Harjunkoski, Iiro. ABB Corporate Research; AlemaniaFil: Grossmann, Ignacio E.. University of Carnegie Mellon. Department of Chemical Engineering; Estados UnidosElsevier2014-03info: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/11336/21765Yongheng, Jiang; Rodriguez, Maria Analia; Harjunkoski, Iiro; Grossmann, Ignacio E.; Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm; Elsevier; Computers and Chemical Engineering; 62; 3-2014; 211-2240098-1354CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compchemeng.2013.11.014info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0098135413003657info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:08:03Zoai:ri.conicet.gov.ar:11336/21765instacron: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 10:08:04.185CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm
title Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm
spellingShingle Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm
Yongheng, Jiang
Supply Chain
Lagrangean Decomposition
Adaptive Piecewise Linearization
title_short Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm
title_full Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm
title_fullStr Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm
title_full_unstemmed Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm
title_sort Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm
dc.creator.none.fl_str_mv Yongheng, Jiang
Rodriguez, Maria Analia
Harjunkoski, Iiro
Grossmann, Ignacio E.
author Yongheng, Jiang
author_facet Yongheng, Jiang
Rodriguez, Maria Analia
Harjunkoski, Iiro
Grossmann, Ignacio E.
author_role author
author2 Rodriguez, Maria Analia
Harjunkoski, Iiro
Grossmann, Ignacio E.
author2_role author
author
author
dc.subject.none.fl_str_mv Supply Chain
Lagrangean Decomposition
Adaptive Piecewise Linearization
topic Supply Chain
Lagrangean Decomposition
Adaptive Piecewise Linearization
dc.description.none.fl_txt_mv In Part I (Rodriguez, Vecchietti, Harjunkoski, & Grossmann, 2014), we proposed an optimization model to redesign the supply chain of spare parts industry under demand uncertainty from strategic and tactical perspectives in a planning horizon consisting of multiple time periods. To address large scale industrial problems, a Lagrangean scheme is proposed to decompose the MINLP of Part I according to the warehouses. The subproblems are first approximated by an adaptive piece-wise linearization scheme that provides lower bounds, and the MILP is further relaxed to an LP to improve solution efficiency while providing a valid lower bound. An initialization scheme is designed to obtain good initial Lagrange multipliers, which are scaled to accelerate the convergence. To obtain feasible solutions, an adaptive linearization scheme is also introduced. The results from an illustrative problem and two real world industrial problems show that the method can obtain optimal or near optimal solutions in modest computational times.
Fil: Yongheng, Jiang. Tsinghua University, Institute of Process Control Engineering; China
Fil: Rodriguez, Maria Analia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Harjunkoski, Iiro. ABB Corporate Research; Alemania
Fil: Grossmann, Ignacio E.. University of Carnegie Mellon. Department of Chemical Engineering; Estados Unidos
description In Part I (Rodriguez, Vecchietti, Harjunkoski, & Grossmann, 2014), we proposed an optimization model to redesign the supply chain of spare parts industry under demand uncertainty from strategic and tactical perspectives in a planning horizon consisting of multiple time periods. To address large scale industrial problems, a Lagrangean scheme is proposed to decompose the MINLP of Part I according to the warehouses. The subproblems are first approximated by an adaptive piece-wise linearization scheme that provides lower bounds, and the MILP is further relaxed to an LP to improve solution efficiency while providing a valid lower bound. An initialization scheme is designed to obtain good initial Lagrange multipliers, which are scaled to accelerate the convergence. To obtain feasible solutions, an adaptive linearization scheme is also introduced. The results from an illustrative problem and two real world industrial problems show that the method can obtain optimal or near optimal solutions in modest computational times.
publishDate 2014
dc.date.none.fl_str_mv 2014-03
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/11336/21765
Yongheng, Jiang; Rodriguez, Maria Analia; Harjunkoski, Iiro; Grossmann, Ignacio E.; Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm; Elsevier; Computers and Chemical Engineering; 62; 3-2014; 211-224
0098-1354
CONICET Digital
CONICET
url http://hdl.handle.net/11336/21765
identifier_str_mv Yongheng, Jiang; Rodriguez, Maria Analia; Harjunkoski, Iiro; Grossmann, Ignacio E.; Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part II: A Lagrangean decomposition algorithm; Elsevier; Computers and Chemical Engineering; 62; 3-2014; 211-224
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/doi/10.1016/j.compchemeng.2013.11.014
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0098135413003657
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/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 13.13397

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