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dc.contributor.authorGijsbrechts, Joren
dc.contributor.authorBoute, Robert
dc.contributor.authorVan Mieghem, Jan A.
dc.contributor.authorZhang, Dennis J.
dc.date.accessioned2022-03-09T08:34:02Z
dc.date.available2022-03-09T08:34:02Z
dc.date.issued2022en_US
dc.identifier.issn1523-4614
dc.identifier.doi10.1287/msom.2021.1064
dc.identifier.urihttp://hdl.handle.net/20.500.12127/7011
dc.description.abstractProblem definition: Is deep reinforcement learning (DRL) effective at solving inventory problems? Academic/practical relevance: Given that DRL has successfully been applied in computer games and robotics, supply chain researchers and companies are interested in its potential in inventory management. We provide a rigorous performance evaluation of DRL in three classic and intractable inventory problems: lost sales, dual sourcing, and multi-echelon inventory management. Methodology: We model each inventory problem as a Markov decision process and apply and tune the Asynchronous Advantage Actor-Critic (A3C) DRL algorithm for a variety of parameter settings. Results: We demonstrate that the A3C algorithm can match the performance of the state-of-the-art heuristics and other approximate dynamic programming methods. Although the initial tuning was computationally demanding and time demanding, only small changes to the tuning parameters were needed for the other studied problems. Managerial implications: Our study provides evidence that DRL can effectively solve stationary inventory problems. This is especially promising when problem-dependent heuristics are lacking. Yet, generating structural policy insight or designing specialized policies that are (ideally provably) near optimal remains desirable.en_US
dc.language.isoenen_US
dc.publisherINFORMSen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectDeep Reinforcement Learningen_US
dc.subjectInventory Controlen_US
dc.subjectDual Sourcingen_US
dc.subjectLost Salesen_US
dc.subjectMulti-Echelonen_US
dc.titleCan deep reinforcement learning improve inventory management? Performance on lost sales, dual-sourcing, and multi-echelon problemsen_US
dc.identifier.journalManufacturing & Service Operations Managementen_US
dc.source.volume24
dc.source.issue3
dc.source.beginpage1349
dc.source.endpage1368
dc.contributor.departmentCatholic University of Portugal (UCP) - Catolica Lisbon School of Business and Economicsen_US
dc.contributor.departmentKU Leuven - Faculty of Business and Economics (FEB)en_US
dc.contributor.departmentNorthwestern University - Kellogg School of Managementen_US
dc.contributor.departmentWashington University in St. Louis - John M. Olin Business Schoolen_US
dc.identifier.eissn1526-5498
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typearticleFT ranked journal article  en_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid102358en_US


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