METHOD OF DYNAMIC MANAGEMENT OF INVENTORY BUFFER BASED ON SOFT CALCULATIONS
DOI:
https://doi.org/10.24025/2306-4412.1.2023.265372Keywords:
theory of constraints, artificial neural network, fuzzy inference systems, CUDA technologyAbstract
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.
References
U. P. Nagarkatte and N. Oley, Theory of Constraints and Thinking Processes for Creative Thinkers: Creative Problem Solving. Boca Raton, Fl: CRC Press, 2018.
B. Sproull, Theory of Constraints, Lean, and Six Sigma Improvement Methodology: Making the Case for Integration. London: CRC Press, 2019.
J. F. Cox, and J. G. Schleher, Theory of Constraints Handbook. New York, NY: McGraw-Hill, 2010.
E. M. Goldratt, "My saga to improve production", Selected Readings Constraints Management. Falls Church, VA: APICS, 1996, pp. 43-48.
E. M. Goldratt, Production: The TOC Way. MA: North River Press, 2003.
S. N. Sivanandam, S. Sumathi, and S. N. Deepa, Introduction to Neural Networks using Matlab 6.0. New Delhi: The McGraw-Hill Comp., Inc., 2006.
S. Haykin, Neural Networks and Learning Machines. Upper Saddle River, New Jersey: Pearson Education, Inc., 2009.
K.-L. Du, and K. M. S. Swamy, Neural Networks and Statistical Learning. London: Springer-Verlag, 2014.
E. Fedorov, O. Nechyporenko, and T. Utkina, "Forecast method for natural language constructions based on a modified gated recursive block", CEUR Workshop Proceedings, vol. 2604, pp. 199-214, 2020.
Z. Zhang, Z. Tang, and C. Vairappan, "A novel learning method for Elman neural network using local search", Neural Information Processing – Letters and Reviews, vol. 11, no. 8, pp. 181-188, 2007.
R. Dey, and F. M. Salem, "Gate-variants of gated recurrent unit (GRU) neural networks", Circuits, Systems, and Neural Networks (CSANN) LAB, Department of Electrical and Computer Engineering Michigan State University, East Lansing, USA, Tech. Report. MI 48824-1226, arXiv: 1701.05923, 2017.
K. Cho, B. van Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation", in Proc. Conf. on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1724-1734.
H. Jaeger, W. Maass, and J. C. Prıncipe, "Special issue on echo state networks and liquid state machines", Neural Networks, vol. 20, no. 3, pp. 287-289, 2007.
A. H. S. Hamdany, R. R. O. Al-Nima, and L. H. Albak, "Translating cuneiform symbols using artificial neural network", TELKOMNIKA Telecommunication, Computing, Electronics and Control, vol. 19, no. 2, pp. 438-443, 2021.
A. Rotshtein, S. Shtovba, and I. Mostav, "Fuzzy rule based innovation projects estimation", in Proc. Joint 9th IFSA World Congress and 20th NAFIPS Int. Conf., 2001, pp. 122-126.
G. P. Reddya, Y. Deepika, K. S. Prasad, and Dr. G. K. Kumar, "Fuzzy logics associated with neural networks in the real time for better world", in Proc. Int. Conf. on Advancements in Aeromechanical Materials for Manufacturing (ICAAMM), 2017, pp. 8507-8516.
V. T. Yen, Y. N. Wang, and P. V. Cuong, "Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators", Neural Computing and Applications, vol. 31, no. 11, pp. 6925-6958, 2019.
H. Das, B. Naik, and H. S. Behera, "Medical disease analysis using neuro-fuzzy with feature extraction model for classification", Informatics in Medicine Unlocked, vol. 18, no. 100288, pp. 1-12, 2020.
Downloads
Published
How to Cite
Issue
Section
URN
License
Copyright (c) 2023 Євген Євгенович Федоров, Ольга Володимирівна Нечипоренко

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors who publish in this journal agree to the following terms:The authors reserve the right to authorship of their work and give the journal the right to first publish this work under the terms of the Creative Commons Attribution License CC BY-NC, which allows other persons to freely distribute published work with a mandatory reference to authors of the original work and the first publication of the work in this journal.
Authors have the right to conclude separate additional agreements for the non-exclusive distribution of the paper in the form in which it was published by this journal (for example, posting work in electronic repository or publishing as part of a monograph), provided that the link to the first publication in this journal is maintained.
The journal policy allows and encourages authors to post on the Internet (for example, in repositories of institutions or on personal websites) the manuscript of work, both before the submission of this manuscript to the editorial staff, and during its editorial work, as it contributes to the emergence of productive scientific discussion and positively affects the efficiency and dynamics of published work citation (see The Effect of Open Access).