A novel hybrid artificial intelligence-based decision support framework to predict lead time

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dc.authoridDosdogru, Ayse Tugba/0000-0002-1548-5237
dc.authoridGOCKEN, Mustafa/0000-0002-1256-2305
dc.authoridBoru Ipek, Asli/0000-0001-6403-5307
dc.contributor.authorDosdogru, Ayse Tugba
dc.contributor.authorBoru Ipek, Asli
dc.contributor.authorGocken, Mustafa
dc.date.accessioned2025-01-06T17:44:33Z
dc.date.available2025-01-06T17:44:33Z
dc.date.issued2021
dc.description.abstractInventory and routing are the two most important elements to company's survival in supply chain environments. Hence, solution approaches of inventory routing problem (IRP) should assure adequate inventory level and also provide an efficient route. In this case, hybrid approaches can empower researchers to solve the IRP. The aim of this study is to develop a new hybrid methodology that includes two phases to provide a generic framework for IRP. In Phase I, genetic algorithm-based simulation optimisation is used to dynamically perform inventory control and routing decisions. In Phase II, artificial intelligence (AI)-based simulation in which the lead time of supply chain members is predicted is employed to extend the functionality of the method in Phase I. The proposed hybrid methodology gives insights into the cross-fertilisation of AI, simulation, and optimisation for researchers. Therefore, this integration can be applied to different supply chain problems by using similar methods.
dc.identifier.doi10.1080/13675567.2020.1749249
dc.identifier.endpage279
dc.identifier.issn1367-5567
dc.identifier.issn1469-848X
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85083586972
dc.identifier.scopusqualityQ1
dc.identifier.startpage261
dc.identifier.urihttps://doi.org/10.1080/13675567.2020.1749249
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3085
dc.identifier.volume24
dc.identifier.wosWOS:000557904900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofInternational Journal of Logistics-Research and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectSupply chain management
dc.subjectinventory routing problem
dc.subjectArtificial intelligence
dc.subjectsimulation optimisation
dc.titleA novel hybrid artificial intelligence-based decision support framework to predict lead time
dc.typeArticle

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