A literature review of supply chain analyses integrating discrete simulation modelling and machine learning

dc.contributor.authorKogler, Christophcs
dc.contributor.authorMaxera, Pavelcs
dc.date.issued2025-05-20cs
dc.description.abstractSimulation and machine learning offer advanced methods to analyse complex flows, risks, and disruptions in supply chains. This literature review, based on a novel classification framework, traces the development of the research area from 2013 to 2025 and confirms intensified publication activities over the past 5 years. A majority of the analysed models merge discrete event simulation with reinforcement learning to cover an operational planning horizon and detailed to intermediate abstraction level. The comprehensive synthesis of 18 review articles, 72 research and conference papers, and 43 related studies explains integration approaches, discusses the current state of the art, and identifies research gaps. Existing individual limitations of discrete simulation and machine learning can be overcome by integrating those essential methods for supply chain analyses. This sets the stage for a new generation of models to plan, design, operate, control, and monitor supply chains in a sustainable, smart, and resilient way.en
dc.description.abstractSimulation and machine learning offer advanced methods to analyse complex flows, risks, and disruptions in supply chains. This literature review, based on a novel classification framework, traces the development of the research area from 2013 to 2025 and confirms intensified publication activities over the past 5 years. A majority of the analysed models merge discrete event simulation with reinforcement learning to cover an operational planning horizon and detailed to intermediate abstraction level. The comprehensive synthesis of 18 review articles, 72 research and conference papers, and 43 related studies explains integration approaches, discusses the current state of the art, and identifies research gaps. Existing individual limitations of discrete simulation and machine learning can be overcome by integrating those essential methods for supply chain analyses. This sets the stage for a new generation of models to plan, design, operate, control, and monitor supply chains in a sustainable, smart, and resilient way.en
dc.formattextcs
dc.format.extent1-25cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJournal of Simulation. 2025, p. 1-25.en
dc.identifier.doi10.1080/17477778.2025.2500393cs
dc.identifier.issn1747-7778cs
dc.identifier.orcid0000-0001-9461-9477cs
dc.identifier.other197890cs
dc.identifier.researcheridV-4736-2017cs
dc.identifier.scopus57205167913cs
dc.identifier.urihttp://hdl.handle.net/11012/251017
dc.language.isoencs
dc.relation.ispartofJournal of Simulationcs
dc.relation.urihttps://www.tandfonline.com/doi/full/10.1080/17477778.2025.2500393cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1747-7778/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectdiscrete event simulation; agent-based simulation; supply chain management; logistics; transportation; superviseden
dc.subjectunsuperviseden
dc.subjectand reinforcement learningen
dc.subjectdiscrete event simulation; agent-based simulation; supply chain management; logistics; transportation; supervised
dc.subjectunsupervised
dc.subjectand reinforcement learning
dc.titleA literature review of supply chain analyses integrating discrete simulation modelling and machine learningen
dc.title.alternativeA literature review of supply chain analyses integrating discrete simulation modelling and machine learningen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-197890en
sync.item.dbtypeVAVen
sync.item.insts2025.10.17 08:57:12en
sync.item.modts2025.10.16 09:32:41en
thesis.grantorVysoké učení technické v Brně. Ústav soudního inženýrství. Odbor znalectví ve strojírenství, analýza dopravních nehod a oceňování motorových vozidelcs

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
A literature review of supply chain analyses integrating discrete simulation modelling and machine learning.pdf
Size:
9.88 MB
Format:
Adobe Portable Document Format
Description:
file A literature review of supply chain analyses integrating discrete simulation modelling and machine learning.pdf