Neuro-fuzzy inference systems approach to decision support system for economic order quantity

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dc.citation.epage1137
dc.citation.spage1114
dc.citation.volume32
dc.contributor.authorSremac, Siniša
dc.contributor.authorZavadskas, Edmundas Kazimieras
dc.contributor.authorMatić, Bojan
dc.contributor.authorKopić, Miloš
dc.contributor.authorStević, Željko
dc.date.accessioned2023-07-14T09:13:01Z
dc.date.available2023-07-14T09:13:01Z
dc.date.issued2019
dc.description.abstractSupply chain management (SCM) has a dynamic structure involving the constant flow of information, product, and funds among different participants. SCM is a complex process and most often characterized by uncertainty. Many values are stochastic and cannot be precisely determined and described by classical mathematical methods. Therefore, in solving real and complex problems individual methods of artificial intelligence are increasingly used, or their combination in the form of hybrid methods. This paper has proposed the decision support system for determining economic order quantity and order implementation based on Adaptive neuro-fuzzy inference systems - ANFIS. A combination of two concepts of artificial intelligence in the form of hybrid neurofuzzy method has been applied into the decision support system in order to exploit the individual advantages of both methods. This method can deal with complexity and uncertainty in SCM better than classical methods because they it stems from experts’ opinions. The proposed decision support system showed good results for determining the amount of economic order and it is presented as a successful tool for planning in SCM. Sensitivity analysis has been applied, which indicates that the decision support system gives valid results. The proposed system is flexible and can be applied to various types of goods in SCM.
dc.identifier.doi10.1080/1331677X.2019.1613249
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/462
dc.language.isoen
dc.publisherTaylor & Francis
dc.sourceEconomic Research-Ekonomska Istraživanja
dc.subjectSupply chain management; neuro-fuzzy; ANFIS; economic order quantity; order implementation
dc.titleNeuro-fuzzy inference systems approach to decision support system for economic order quantity
dc.typeArticle
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