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dc.contributor.authorMousavi Abdehgah, Mohosen
dc.contributor.authorPardalos, Panos M.
dc.contributor.authorNiaki, Seyed Taghi Akhavan
dc.contributor.authorFügenschuh, Armin
dc.contributor.authorFathi, Mahdi
dc.date.accessioned2019-02-11T09:30:08Z
dc.date.issued2019fi
dc.identifier.citationMousavi Abdehgah, M., Pardalos, P. M., Niaki, S. T. A., Fügenschuh, A., & Fathi, M. (2019). Solving a continuous periodic review inventory-location allocation problem in vendor-buyer supply chain under uncertainty. <em>Computers and Industrial Engineering</em>, 128, 541-552. <a href="https://doi.org/10.1016/j.cie.2018.12.071">doi:10.1016/j.cie.2018.12.071</a>fi
dc.identifier.otherTUTKAID_80447
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/62737
dc.description.abstractIn this work, a mixed-integer binary non-linear two-echelon inventory problem is formulated for a vendor-buyer supply chain network in which lead times are constant and the demands of buyers follow a normal distribution. In this formulation, the problem is a combination of an (r, Q) and periodic review policies based on which an order of size Q is placed by a buyer in each fixed period once his/her on hand inventory reaches the reorder point r in that period. The constraints are the vendors’ warehouse spaces, production restrictions, and total budget. The aim is to find the optimal order quantities of the buyers placed for each vendor in each period alongside the optimal placement of the vendors among the buyers such that the total supply chain cost is minimized. Due to the complexity of the problem, a Modified Genetic Algorithm (MGA) and a Particle Swarm Optimization (PSO) are used to find optimal and near-optimum solutions. In order to assess the quality of the solutions obtained by the algorithms, a mixed integer nonlinear program (MINLP) of the problem is coded in GAMS. A design of experiment approach named Taguchi is utilized to adjust the parameters of the algorithms. Finally, a wide range of numerical illustrations is generated and solved to evaluate the performances of the algorithms. The results show that the MGA outperforms the PSO in terms of the fitness function in most of the problems and also is faster than the PSO in terms of CPU time in all the numerical examples.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherPergamon Press
dc.relation.ispartofseriesComputers and Industrial Engineering
dc.rightsCC BY-NC-ND 4.0
dc.subject.othergeneettiset algorithmitfi
dc.subject.othertoimitusketjutfi
dc.subject.otherinventory-location allocation problemfi
dc.subject.othermixed-integer binary non-linear programmingfi
dc.subject.othertwo-echelon supply chainfi
dc.subject.otherstochastic demandsfi
dc.subject.othergenetic algorithmfi
dc.titleSolving a continuous periodic review inventory-location allocation problem in vendor-buyer supply chain under uncertaintyfi
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201901231292
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.date.embargo2022-03-01
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2019-01-23T13:15:12Z
dc.description.reviewstatuspeerReviewed
dc.format.pagerange541-552
dc.relation.issn0360-8352
dc.relation.numberinseries0
dc.relation.volume128
dc.type.versionFinal Draft
dc.rights.copyright© 2018 Published by Elsevier Ltd.
dc.rights.accesslevelopenAccessfi
dc.format.contentfulltext
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.cie.2018.12.071


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