Deep reinforcement learning for inventory control: a roadmap
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.14/34576 |
Resumo: | Deep reinforcement learning (DRL) has shown great potential for sequential decision-making, including early developments in inventory control. Yet, the abundance of choices that come with designing a DRL algorithm, combined with the intense computational effort to tune and evaluate each choice, may hamper their application in practice. This paper describes the key design choices of DRL algorithms to facilitate their implementation in inventory control. We also shed light on possible future research avenues that may elevate the current state-of-the-art of DRL applications for inventory control and broaden their scope by leveraging and improving on the structural policy insights within inventory research. Our discussion and roadmap may also spur future research in other domains within operations management. |
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Deep reinforcement learning for inventory control: a roadmapInventory managementMachine learningNeural networksReinforcement learningDeep reinforcement learning (DRL) has shown great potential for sequential decision-making, including early developments in inventory control. Yet, the abundance of choices that come with designing a DRL algorithm, combined with the intense computational effort to tune and evaluate each choice, may hamper their application in practice. This paper describes the key design choices of DRL algorithms to facilitate their implementation in inventory control. We also shed light on possible future research avenues that may elevate the current state-of-the-art of DRL applications for inventory control and broaden their scope by leveraging and improving on the structural policy insights within inventory research. Our discussion and roadmap may also spur future research in other domains within operations management.Veritati - Repositório Institucional da Universidade Católica PortuguesaBoute, Robert N.Gijsbrechts, JorenJaarsveld, Willem vanVanvuchelen, Nathalie2021-09-01T13:34:58Z2022-04-162022-04-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/34576eng0377-221710.1016/j.ejor.2021.07.01685111846139000742431300001info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-16T01:42:45Zoai:repositorio.ucp.pt:10400.14/34576Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:28:01.648012Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Deep reinforcement learning for inventory control: a roadmap |
title |
Deep reinforcement learning for inventory control: a roadmap |
spellingShingle |
Deep reinforcement learning for inventory control: a roadmap Boute, Robert N. Inventory management Machine learning Neural networks Reinforcement learning |
title_short |
Deep reinforcement learning for inventory control: a roadmap |
title_full |
Deep reinforcement learning for inventory control: a roadmap |
title_fullStr |
Deep reinforcement learning for inventory control: a roadmap |
title_full_unstemmed |
Deep reinforcement learning for inventory control: a roadmap |
title_sort |
Deep reinforcement learning for inventory control: a roadmap |
author |
Boute, Robert N. |
author_facet |
Boute, Robert N. Gijsbrechts, Joren Jaarsveld, Willem van Vanvuchelen, Nathalie |
author_role |
author |
author2 |
Gijsbrechts, Joren Jaarsveld, Willem van Vanvuchelen, Nathalie |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Boute, Robert N. Gijsbrechts, Joren Jaarsveld, Willem van Vanvuchelen, Nathalie |
dc.subject.por.fl_str_mv |
Inventory management Machine learning Neural networks Reinforcement learning |
topic |
Inventory management Machine learning Neural networks Reinforcement learning |
description |
Deep reinforcement learning (DRL) has shown great potential for sequential decision-making, including early developments in inventory control. Yet, the abundance of choices that come with designing a DRL algorithm, combined with the intense computational effort to tune and evaluate each choice, may hamper their application in practice. This paper describes the key design choices of DRL algorithms to facilitate their implementation in inventory control. We also shed light on possible future research avenues that may elevate the current state-of-the-art of DRL applications for inventory control and broaden their scope by leveraging and improving on the structural policy insights within inventory research. Our discussion and roadmap may also spur future research in other domains within operations management. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-01T13:34:58Z 2022-04-16 2022-04-16T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.14/34576 |
url |
http://hdl.handle.net/10400.14/34576 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0377-2217 10.1016/j.ejor.2021.07.016 85111846139 000742431300001 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131998682349568 |