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Can deep reinforcement learning improve inventory management? Performance on lost sales, dual-sourcing, and multi-echelon problems

dc.contributor.authorGijsbrechts, Joren
dc.contributor.authorBoute, Robert N.
dc.contributor.authorMieghem, Jan A. van
dc.contributor.authorZhang, Dennis J.
dc.date.accessioned2022-07-01T08:36:34Z
dc.date.available2022-07-01T08:36:34Z
dc.date.issued2022-05
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.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.doi10.1287/msom.2021.1064pt_PT
dc.identifier.eid85132222235
dc.identifier.issn1523-4614
dc.identifier.urihttp://hdl.handle.net/10400.14/38036
dc.identifier.wos000803569300001
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectOM-information technology interfacept_PT
dc.subjectInventory theory and controlpt_PT
dc.subjectLogistics and transportationpt_PT
dc.subjectSupply chain managementpt_PT
dc.titleCan deep reinforcement learning improve inventory management? Performance on lost sales, dual-sourcing, and multi-echelon problemspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage1368pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage1349pt_PT
oaire.citation.titleManufacturing and Service Operations Managementpt_PT
oaire.citation.volume24pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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