Method of dynamic management of inventory buffer based on soft calculations
Abstract
Currently, more and more companies seek to improve and optimize their business processes based on the implementation of the technology of the theory of constraints, which provides dynamic management of the inventory buffer and is used to manage supply chains. As a result, the relevance of the development of methods of intellectualization of the technology of the theory of constraints is increasing significantly. To date, there are no computer systems for dynamic management by the inventory buffer, which are based on soft calculations. The aim of the work is to improve the efficiency of dynamic management of the inventory buffer by means of an artificial neuro-fuzzy network, which is trained on the basis of the back-propagation method. In order to solve the problem of increasing the efficiency of the dynamic management of the inventory buffer, appropriate methods of artificial intelligence were investigated. Research data has shown that the most effective method today is the use of artificial neural networks in combination with a fuzzy inference system. The paper proposes a method of dynamic management of the inventory buffer based on soft calculations. The novelty of the research is that for dynamic management of the inventory buffer a method based on fuzzy logic and an artificial neural network, and also two models of artificial neuro-fuzzy network of the dynamic management of the inventory buffer have been created, three criteria for evaluating the effectiveness of the proposed models have been selected, the parameters of the proposed models based on the method of back propagation in batch mode, oriented on the technology of information parallel processing, have been identified. As a result of numerical study, it is established that the proposed method of neuro-fuzzy dynamic management of the inventory buffer provides a probability of incorrectly made decisions regarding the dynamic management of the inventory buffer of 0.07, and a root mean square error of 0.10. The proposed models and procedures for their parametric identification allow to increase the speed, accuracy and reliability of decision-making. The proposed method of dynamic management of the inventory buffer based on soft calculations can be used in various intelligent systems
Keywords
theory of constraints; artificial neural network; fuzzy inference systems; CUDA technology
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