Neuro-Fuzzy Model in Supply Chain Management for Objects State Assessing - Mikhail Mikhailovich Chupin; Alexey Sergeevich Katasev; Amir Muratovich Akhmetvaleev; Dina Vladimirovna Kataseva (International Journal of Supply Chain Management, 2019)
This article considers the task of objects state assessing in conditions of uncertainty by considering the supply chain strategy. To solve it, the need to use fuzzy-production knowledge bases and fuzzy inference algorithms as part of fuzzy decision support systems is being updated. As a tool for constructing a knowledge base, a neural-fuzzy model is proposed. The proposed type of fuzzy-production rules and the logic inference algorithm on rules for objects state assessing are described. A structure of a fuzzy neural network, consisting of six layers, each of which implements the corresponding stage of the logic inference algorithm, is proposed. As a result of training a fuzzy neural network, a system of fuzzy-production rules is formed, which make up the knowledge base of the decision support system for objects state assessing. On the basis of the proposed neuro-fuzzy model, a software package has been implemented for automating the processes of forming fuzzy-production rules. The main components of the software package are the knowledge base generation module and the fuzzy inference module. As an approbation of the neuro-fuzzy model, the formation of fuzzy rules for assessing the state of water lines at the cluster pumping stations in reservoir pressure maintenance systems has been carried out. The testing results confirmed the high efficiency of the neural-fuzzy model and the possibility of its practical use for the formation of fuzzy-production rules in various subject areas of human activity.