A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem

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dc.authoridGOCKEN, Mustafa/0000-0002-1256-2305
dc.authoridDosdogru, Ayse Tugba/0000-0002-1548-5237
dc.authoridBoru Ipek, Asli/0000-0001-6403-5307
dc.contributor.authorBoru, Asli
dc.contributor.authorDosdogru, Ayse Tugba
dc.contributor.authorGocken, Mustafa
dc.contributor.authorErol, Rizvan
dc.date.accessioned2025-01-06T17:44:21Z
dc.date.available2025-01-06T17:44:21Z
dc.date.issued2019
dc.description.abstractIn this paper, a new hybrid method including simulation optimization and artificial intelligence based simulation is created to solve the inventory routing problem (IRP) in which three different routing strategies are evaluated for uneven demand patterns including intermittent, erratic, and lumpy demand. The proposed method includes two phases. In the first phase, a nondominated sorting genetic algorithm II based simulation is employed to perform a multi-objective search for the IRP where the objectives of the method are total supply chain cost minimization and average service level maximization. In the second phase, artificial neural network based simulation is used to adjust the reorder point and order-up-to-level by forecasting the customer demand at each replenishment time. The results of the study demonstrated that the average service level is at least 98.54% in the supply chain. From this, it can be concluded that the proposed method can provide a tremendous opportunity to improve the average service level under uncertain environments. In addition, it is determined that different routing strategies can be selected for different demand patterns according to the considered performance measures.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)
dc.description.sponsorshipAsli Boru gratefully acknowledges the Scientific and Technological Research Council of Turkey (TUBITAK) for the 2211-C PhD scholarship program.
dc.identifier.doi10.3390/sym11050717
dc.identifier.issn2073-8994
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85066316953
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/sym11050717
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3024
dc.identifier.volume11
dc.identifier.wosWOS:000470990900120
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSymmetry-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectsimulation optimization
dc.subjectartificial intelligence
dc.subjectsupply chain
dc.subjectdemand forecasting
dc.subjectrouting strategies
dc.titleA Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem
dc.typeArticle

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