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dc.contributor.author
Arredondo, Facundo
dc.contributor.author
Martínez, Ernesto Carlos
dc.date.available
2019-09-18T13:33:41Z
dc.date.issued
2010-02
dc.identifier.citation
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
dc.identifier.issn
0360-8352
dc.identifier.uri
http://hdl.handle.net/11336/83826
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Pergamon-Elsevier Science Ltd
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Order Acceptance
dc.subject
Reinforcement Learning
dc.subject
Revenue Management
dc.subject
Make-To-Orde Manufacturing
dc.subject.classification
Ingeniería de Procesos Químicos
dc.subject.classification
Ingeniería Química
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-09-17T13:55:50Z
dc.journal.volume
58
dc.journal.number
1
dc.journal.pagination
70-83
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
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
dc.description.fil
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
dc.journal.title
Computers & Industrial Engineering
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.cie.2009.08.005


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