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

• Published in: Economic research - Ekonomska istraživanja Vol. 32; no. 1; pp. 1114 - 1137
• Main Authors: Sremac, Siniša, Zavadskas, Edmundas Kazimieras, Matić, Bojan, Kopić, Miloš, Stević, Željko
• Format: Journal Article Paper
• Language: English
• Published: Pula Routledge 2019; Taylor & Francis Ltd; Taylor and Francis Group i Sveučilište Jurja Dobrile u Puli, Fakultet ekonomije i turizma Dr. Mijo Mirković
• Subjects: ANFIS; Artificial intelligence; Decision support systems; economic order quantity; Economic theory; Experts; Inference; neuro-fuzzy; order implementation; Order quantity; Sensitivity analysis; Supply; Supply chain management; Systems approach; Uncertainty
• ISSN: 1331-677X; 1848-9664
• DOI: 10.1080/1331677X.2019.1613249

Supply 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 neuro-fuzzy 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.