Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing

Autores
Arredondo, Facundo; Martínez, Ernesto Carlos
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Order acceptance under uncertainty is a critical decision-making problem at the interface between customer relationship management and production planning of order-driven manufacturing systems. In this work, a novel approach for simulation-based development and on-line adaptation of a policy for dynamic order acceptance under uncertainty in make-to-order manufacturing using average-reward reinforcement learning is proposed. Locally weighted regression is used to generalize the gain value of accepting or rejecting similar orders regarding attributes such as product mix, price, size and due date. The order acceptance policy is learned by classifying an arriving order as belonging either to the acceptance set or to the rejection set. For exploitation, only orders in the acceptance set must be chosen for shop-floor scheduling. For exploration some orders from the rejection set are also considered as candidates for acceptance. Comparisons made with different order acceptance heuristics highlight the effectiveness of the proposed ARLOA algorithm to maximize the average revenue obtained per unit cost of installed capacity whilst quickly responding to unknown variations in order arrival rates and attributes.
Fil: Arredondo, Facundo. 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: Martínez, Ernesto Carlos. 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
Materia
Order Acceptance
Reinforcement Learning
Revenue Management
Make-To-Orde Manufacturing
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:11336/83826
Acceder
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spelling Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturingArredondo, FacundoMartínez, Ernesto CarlosOrder AcceptanceReinforcement LearningRevenue ManagementMake-To-Orde Manufacturinghttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Order acceptance under uncertainty is a critical decision-making problem at the interface between customer relationship management and production planning of order-driven manufacturing systems. In this work, a novel approach for simulation-based development and on-line adaptation of a policy for dynamic order acceptance under uncertainty in make-to-order manufacturing using average-reward reinforcement learning is proposed. Locally weighted regression is used to generalize the gain value of accepting or rejecting similar orders regarding attributes such as product mix, price, size and due date. The order acceptance policy is learned by classifying an arriving order as belonging either to the acceptance set or to the rejection set. For exploitation, only orders in the acceptance set must be chosen for shop-floor scheduling. For exploration some orders from the rejection set are also considered as candidates for acceptance. Comparisons made with different order acceptance heuristics highlight the effectiveness of the proposed ARLOA algorithm to maximize the average revenue obtained per unit cost of installed capacity whilst quickly responding to unknown variations in order arrival rates and attributes.Fil: Arredondo, Facundo. 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: Martínez, Ernesto Carlos. 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; ArgentinaPergamon-Elsevier Science Ltd2010-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/83826Arredondo, Facundo; Martínez, Ernesto Carlos; Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing; Pergamon-Elsevier Science Ltd; Computers & Industrial Engineering; 58; 1; 2-2010; 70-830360-8352CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.cie.2009.08.005info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:00:19Zoai:ri.conicet.gov.ar:11336/83826instacron: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:34982025-10-22 11:00:19.751CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
title Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
spellingShingle Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
Arredondo, Facundo
Order Acceptance
Reinforcement Learning
Revenue Management
Make-To-Orde Manufacturing
title_short Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
title_full Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
title_fullStr Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
title_full_unstemmed Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
title_sort Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
dc.creator.none.fl_str_mv Arredondo, Facundo
Martínez, Ernesto Carlos
author Arredondo, Facundo
author_facet Arredondo, Facundo
Martínez, Ernesto Carlos
author_role author
author2 Martínez, Ernesto Carlos
author2_role author
dc.subject.none.fl_str_mv Order Acceptance
Reinforcement Learning
Revenue Management
Make-To-Orde Manufacturing
topic Order Acceptance
Reinforcement Learning
Revenue Management
Make-To-Orde Manufacturing
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Order acceptance under uncertainty is a critical decision-making problem at the interface between customer relationship management and production planning of order-driven manufacturing systems. In this work, a novel approach for simulation-based development and on-line adaptation of a policy for dynamic order acceptance under uncertainty in make-to-order manufacturing using average-reward reinforcement learning is proposed. Locally weighted regression is used to generalize the gain value of accepting or rejecting similar orders regarding attributes such as product mix, price, size and due date. The order acceptance policy is learned by classifying an arriving order as belonging either to the acceptance set or to the rejection set. For exploitation, only orders in the acceptance set must be chosen for shop-floor scheduling. For exploration some orders from the rejection set are also considered as candidates for acceptance. Comparisons made with different order acceptance heuristics highlight the effectiveness of the proposed ARLOA algorithm to maximize the average revenue obtained per unit cost of installed capacity whilst quickly responding to unknown variations in order arrival rates and attributes.
Fil: Arredondo, Facundo. 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: Martínez, Ernesto Carlos. 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
description Order acceptance under uncertainty is a critical decision-making problem at the interface between customer relationship management and production planning of order-driven manufacturing systems. In this work, a novel approach for simulation-based development and on-line adaptation of a policy for dynamic order acceptance under uncertainty in make-to-order manufacturing using average-reward reinforcement learning is proposed. Locally weighted regression is used to generalize the gain value of accepting or rejecting similar orders regarding attributes such as product mix, price, size and due date. The order acceptance policy is learned by classifying an arriving order as belonging either to the acceptance set or to the rejection set. For exploitation, only orders in the acceptance set must be chosen for shop-floor scheduling. For exploration some orders from the rejection set are also considered as candidates for acceptance. Comparisons made with different order acceptance heuristics highlight the effectiveness of the proposed ARLOA algorithm to maximize the average revenue obtained per unit cost of installed capacity whilst quickly responding to unknown variations in order arrival rates and attributes.
publishDate 2010
dc.date.none.fl_str_mv 2010-02
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/11336/83826
Arredondo, Facundo; Martínez, Ernesto Carlos; Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing; Pergamon-Elsevier Science Ltd; Computers & Industrial Engineering; 58; 1; 2-2010; 70-83
0360-8352
CONICET Digital
CONICET
url http://hdl.handle.net/11336/83826
identifier_str_mv Arredondo, Facundo; Martínez, Ernesto Carlos; Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing; Pergamon-Elsevier Science Ltd; Computers & Industrial Engineering; 58; 1; 2-2010; 70-83
0360-8352
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.cie.2009.08.005
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
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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 13.229304

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