Heuristic-based neural networks for stochastic dynamic lot sizing problem

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dc.authoridSenyigit, Ercan/0000-0002-9388-2633
dc.authoridAydin, Mehmet/0000-0002-4890-5648
dc.contributor.authorSenyigit, Ercan
dc.contributor.authorDugenci, Muharrem
dc.contributor.authorAydin, Mehmet E.
dc.contributor.authorZeydan, Mithat
dc.date.accessioned2024-09-29T15:55:04Z
dc.date.available2024-09-29T15:55:04Z
dc.date.issued2013
dc.departmentKarabük Üniversitesien_US
dc.description.abstractMulti-period single-item lot sizing problem under stochastic environment has been tackled by few researchers and remains in need of further studies. It is mathematically intractable due to its complex structure. In this paper, an optimum lot-sizing policy based on minimum total relevant cost under price and demand uncertainties was studied by using various artificial neural networks trained with heuristic-based learning approaches; genetic algorithm (GA) and bee algorithm (BA). These combined approaches have been examined with three domain-specific costing heuristics comprising revised silver meal (RSM), revised least unit cost (RLUC), cost benefit (CB). It is concluded that the feed-forward neural network (FF-NN) model trained with BA outperforms the other models with better prediction results. In addition, RLUC is found the best operating domain-specific heuristic to calculate the total cost incurring of the lot-sizing problem. Hence, the best paired heuristics to help decision makers are suggested as RLUC and FF-NN trained with BA. (C) 2012 Elsevier B. V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2012.02.026
dc.identifier.endpage1339en_US
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-84881663536en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1332en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2012.02.026
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4425
dc.identifier.volume13en_US
dc.identifier.wosWOS:000314664900002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectStochastic lot-sizingen_US
dc.subjectFeed-forward neural networksen_US
dc.subjectBee algorithmen_US
dc.subjectGenetic algorithmsen_US
dc.subjectTaguchi methodsen_US
dc.titleHeuristic-based neural networks for stochastic dynamic lot sizing problemen_US
dc.typeArticleen_US

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