Reducing demand signal variability via a quantitative fuzzy grey regression approach

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dc.authorid0000-0002-0479-6937
dc.contributor.authorTozan, Hakan
dc.contributor.authorKarataş, Mümtaz
dc.contributor.authorVayvay, Özalp
dc.date.accessioned10.07.201910:49:13
dc.date.accessioned2019-07-10T19:50:14Z
dc.date.available10.07.201910:49:13
dc.date.available2019-07-10T19:50:14Z
dc.date.issued2018
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.descriptionWOS: 000445262900023
dc.description.abstractThe total system performance of dynamic and complex supply chain networks depends mainly on accurate demand signal estimation as incorporated with an appropriate decision-making process. Due to the field of activity and architecture, however, it is hard to choose a proper forecasting and demand decision model that would befit the complexity of the system. This paper develops a conjoint intelligent hybrid system, comprised of an adaptive neuro-fuzzy inference system (ANFIS) based demand decision process, integrated with crisp grey GM (1,1) and fuzzy grey regression (FGR) forecasting models. We adopt this approach in an attempt to reduce the demand signal variability in supply-chain networks and to evaluate the system response to the proposed models under predefined, relatively low, medium and high demand signal variations. The results obtained from the simulation runs illustrate that the proposed hybrid system reduces the variability considerably; and also, could be considered as a substantial tool for reduction of supply chain phenomenon so called Bullwhip effect.
dc.identifier.citationTozan, H., Karataş, M. ve Vayvay, Ö. (2018). Reducing demand signal variability via a quantitative fuzzy grey regression approach. Tehnicki Vjesnik-Technical Gazette, 25(Supplement: 2), 411-419. https://dx.doi.org/10.17559/TV-20171115130250
dc.identifier.doi10.17559/TV-20171115130250
dc.identifier.endpage419
dc.identifier.issn1330-3651
dc.identifier.issn1848-6339
dc.identifier.issueSupplement: 2
dc.identifier.scopusqualityQ2
dc.identifier.startpage411
dc.identifier.urihttps://dx.doi.org/10.17559/TV-20171115130250
dc.identifier.urihttps://hdl.handle.net/20.500.12511/1925
dc.identifier.volume25
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherStrojarski Facultet
dc.relation.ispartofTehnicki Vjesnik-Technical Gazetteen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectANFIS
dc.subjectDemand Signal Processing
dc.subjectFuzzy Forecasting
dc.subjectFuzzy Logic
dc.subjectGrey Systems
dc.titleReducing demand signal variability via a quantitative fuzzy grey regression approach
dc.typeArticle

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