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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/83826
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:32:20Zoai: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:34982025-09-29 09:32:20.804CONICET 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 |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |