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International Journal of Neutrosophic Science
Volume 21 , Issue 2, PP: 142-160 , 2023 | Cite this article as | XML | Html |PDF

Title

Neutrosophic-based Machine Learning Techniques in the Context of Supply Chain Management: A Survey

  Esraa Kamal 1 * ,   Amal F. Abdel-Gawad 2 ,   Shereen Zaki 3

1  Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt
    (esraakamal183@gmail.com)

2  Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt
    (amgawad2001@yahoo.com)

3  Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt
    (szsoliman@zu.edu.eg)

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Doi   :   https://doi.org/10.54216/IJNS.210213

Received: February 12, 2023 Revised: May 01, 2023 Accepted: June 04, 2023

Abstract :

Supply Chain Management (SCM) plays a critical role in the success of any business organization. Individuals involved in business activities often have to make decisions regarding different aspects of the supply chain, including planning, procurement, production, inventory management, transportation, distribution, and customer relationship management. The combination of neutrosophic logic and machine learning has gained significant attention in the field of SCM as a means to tackle uncertainties and improve decision-making. This paper highlights the potential benefits and applications of integrating neutrosophic reasoning and machine learning in SCM. Neutrosophic reasoning provides a framework for handling imprecise and uncertain information, while machine learning techniques offer powerful tools for data analysis, pattern recognition, and predictive modeling. By leveraging machine learning algorithms within the context of neutrosophic logic, SCM practitioners can enhance demand forecasting accuracy, optimize inventory management, improve transportation and routing decisions, and strengthen supply chain collaboration. The integration of neutrosophic logic and machine learning enables the handling of complex supply chain data, accommodates dynamic and uncertain environments, and supports proactive decision-making. Furthermore, the combination of these approaches can contribute to improved supply chain resilience, sustainability, and customer satisfaction. This paper applied the neutrosophic AHP method as a feature section to select the highest importance criteria as an input to machine learning. Then we applied two machine learning models named random forest and decision. The results show the random forest has the highest accuracy followed by a decision tree.

Keywords :

Machine Learning; Supply Chain Management; Neutrosophic Logic; Neutrosophic AHP; SCM.

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Cite this Article as :
Style #
MLA Esraa Kamal, Amal F. Abdel-Gawad, Shereen Zaki. "Neutrosophic-based Machine Learning Techniques in the Context of Supply Chain Management: A Survey." International Journal of Neutrosophic Science, Vol. 21, No. 2, 2023 ,PP. 142-160 (Doi   :  https://doi.org/10.54216/IJNS.210213)
APA Esraa Kamal, Amal F. Abdel-Gawad, Shereen Zaki. (2023). Neutrosophic-based Machine Learning Techniques in the Context of Supply Chain Management: A Survey. Journal of International Journal of Neutrosophic Science, 21 ( 2 ), 142-160 (Doi   :  https://doi.org/10.54216/IJNS.210213)
Chicago Esraa Kamal, Amal F. Abdel-Gawad, Shereen Zaki. "Neutrosophic-based Machine Learning Techniques in the Context of Supply Chain Management: A Survey." Journal of International Journal of Neutrosophic Science, 21 no. 2 (2023): 142-160 (Doi   :  https://doi.org/10.54216/IJNS.210213)
Harvard Esraa Kamal, Amal F. Abdel-Gawad, Shereen Zaki. (2023). Neutrosophic-based Machine Learning Techniques in the Context of Supply Chain Management: A Survey. Journal of International Journal of Neutrosophic Science, 21 ( 2 ), 142-160 (Doi   :  https://doi.org/10.54216/IJNS.210213)
Vancouver Esraa Kamal, Amal F. Abdel-Gawad, Shereen Zaki. Neutrosophic-based Machine Learning Techniques in the Context of Supply Chain Management: A Survey. Journal of International Journal of Neutrosophic Science, (2023); 21 ( 2 ): 142-160 (Doi   :  https://doi.org/10.54216/IJNS.210213)
IEEE Esraa Kamal, Amal F. Abdel-Gawad, Shereen Zaki, Neutrosophic-based Machine Learning Techniques in the Context of Supply Chain Management: A Survey, Journal of International Journal of Neutrosophic Science, Vol. 21 , No. 2 , (2023) : 142-160 (Doi   :  https://doi.org/10.54216/IJNS.210213)
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