Yapay Zekâ ve Makine Öğrenimi Teknolojilerinin Lojistik Sektöründe Kullanımına Yönelik Bir İnceleme

Year 2024, , 218 - 225, 15.10.2024

Suzan Oğuz , Deniz Yalçıntaş

https://doi.org/10.16951/trendbusecon.1494826

Abstract

Son yıllarda yapay zekâ ve makine öğrenimi teknolojilerindeki gelişmelerin tüm sektörlerde derin etkileri bulunmaktadır. Lojistik sektörü de bu teknolojilerin önemli ölçüde kullanıldığı bir sektör haline gelmiştir. Akıllı lojistik sistemlerinin ortaya çıkışı, tedarik zinciri ve lojistik alanında dijital dönüşümü kolaylaştırmasıyla bu sektörün ilerlemesi için çeşitli fırsatlar sunmaktadır. Bu çalışmanın amacı lojistik sektöründe yapay zekâ ve makine öğrenimi sistemlerinin kullanımını inceleyen güncel çalışmaları kapsamlı bir şekilde incelemektir. Sistematik bir çalışma olarak tasarlanan bu çalışmada öncelikle mevcut literatürden yola çıkılarak lojistik sektöründe yapay zekâ ve makine öğrenimi sistemleri ile ilgili temel kavramlar, eğilimler, bu konuda çalışma yapan araştırmacılar ve ülkeler bibliyometrik analiz yöntemiyle incelenmiştir. Daha sonra lojistikte öne çıkan yapay zekâ ve makine öğrenimi sistemleri ile ilgili bilgilere yer verilmiştir. Lojistikte en sık kullanılan yapay zeka ve makine öğrenimi teknolojilerinin derin öğrenme, optimizasyon, nesnelerin interneti, veri madenciliği ve tahmin modelleri olduğu görülmektedir. Çalışmada sunulan metodolojiler lojistikte verimliliği, şeffaflığı ve planlanmayı arttırmada pratik bir öneme sahiptir.

Keywords

Lojistik, Yapay Zekâ, Makine Öğrenimi, Teknoloji, Bibliyometrik Analiz

A Review on The Use of Artificial Intelligence and Machine Learning Technologies in The Logistics Sector

Abstract

In recent years, developments in Artificial Intelligence (AI) and MachineLearning (ML) technologies have had profound effects on all sectors. The logistics industry has also become a sector where these technologies are being used to a significant extent. The emergence of intelligent logistics systems offers several opportunities for the advancement of this sector by facilitating digital transformation in supply chain and logistics. The aim of this study is to provide a comprehensive review of recent studies examining the use of AI and ML systems in the logistics industry. In this study, which is designed as a systematic study, firstly, based on the existing literature, the basic concepts, trends, researchers and countries working on AI and ML systems in the logistics sector are examined by bibliometric analysis method. Then, information about the prominent AI and ML systems in logistics is given. It is seen that the most frequently used AI and ML technologies in logistics are Deep Learning, Optimization, Internet of Things (IoT), Data Mining and Predictive Models. The methodologies presented in the study have a practical importance in increasing efficiency, transparency and planning in the logistics.

Keywords

Logistics, Artificial Intelligence, Machine Learning, Technology, Bibliometric Analysis

References

• Abosuliman, S. S., & Almagrabi, A. O. (2021). Computer vision assisted human computer interaction for logistics management using Deep Learning. Computers & Electrical Engineering, 96, 107555. [CrossRef]
• Boujarra, M., Lechhab, A., Al Karkouri, A., Zrigui, I., Fakhri, Y., & Bourekkadi, S. (2024). Revolutionizing logistics through Deep Learning: Innovative solutions to optimize data security. Journal of Theoretical and Applied Information Technology, 102(4), 1593-1607. [CrossRef]
• Boukerche, A., & Wang, J. (2020). Machine learning-based traffic prediction models for intelligent transportation systems. Computer Networks, 181, 107530. [CrossRef]
• Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4–13. [CrossRef]
• Çetiner, Ö. (2024). Avrupa yeşil mutabakati konusundaki akademik çalişmalarin görsel haritalama tekniği ile bibliyometrik analizi. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(1), 275-295. [CrossRef]
• Che, C., Liu, B., Li, S., Huang, J., & Hu, H. (2023). Deep Learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10), 36-41. [CrossRef]
• Cheah, J. H., Kersten, W., Ringle, C. M., & Wallenburg, C. (2023). Guest editorial: Predictive modeling in logistics and supply chain management research using partial least squares structural equation modeling. International Journal of Physical Distribution & Logistics Management, 53(7/8), 709-717. [CrossRef]
• Chin, W., Cheah, J. H., Liu, Y., Ting, H., Lim, X. J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management & Data Systems, 120(12), 2161-2209. [CrossRef]
• Chung, S. H. (2021). Applications of smart technologies in logistics and transport: A review. Transportation Research Part E: Logistics and Transportation Review, 153, 102455. [CrossRef]
• Congna, Q., Huifeng, Z., & Bo, L. (2009). Study on Application of Data Mining Technology to Modern Logistics Management Decision. 2009 International Forum on Information Technology and Applications, 433-436. [CrossRef]
• Daim, T.U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73, 981–1012. [CrossRef]
• Guerrero‐Ibañez, J., Contreras‐Castillo, J., & Zeadally, S. (2021). Deep Learning support for intelligent transportation systems. Transactions on Emerging Telecommunications Technologies, 32(3), 4169. [CrossRef]
• Jiang, F., Ma, X. Y., Zhang, Y. H., Wang, L., Cao, W. L., Li, J. X., & Tong, J. (2022). A new form of Deep Learning in smart logistics with IoT environment. The Journal of Supercomputing, 78(9), 11873-11894. [CrossRef]
• Kara, M. E., Fırat, S. Ü. O., & Ghadge, A. (2020). A data mining-based framework for supply chain risk management. Computers & Industrial Engineering, 139, 105570. [CrossRef]
• Koot, M., Mes, M. R. K., & Iacob, M. E. (2021). A systematic literature review of supply chain decision making supported by the Internet of Things and Big Data Analytics. Computers & Industrial Engineering, 154, 107076. [CrossRef]
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444. [CrossRef]
• Lu, J., & Han, X. (2020). An experimental model of Deep Learning logistics distribution based on internet of things. Sensors & Transducers, 242(3), 6-11. [CrossRef]
• Mei, L.-B., & Wang, Q. (2021). Structural optimization in civil engineering: A literature review. Buildings, 11(2), 66. [CrossRef]
• Merigó, J.M., Gil-Lafuente, A.M., & Yager, R.R. (2015). An overview of fuzzy research with bibliometric indicators. Applied Soft Computing, 27, 420–433. [CrossRef]
• Muchová, M., Paralič, J., & Nemčík, M. (2018). Using predictive data mining models for data analysis in a logistics company. In Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology–ISAT 2017: Part I (pp. 161-170). Springer International Publishing. [CrossRef]
• Ranjan, J., & Bhatnagar, V. (2011). Role of knowledge management and analytical CRM in business: data mining based framework. The Learning Organization, 18(2), 131-148. [CrossRef]
• Rejeb, A., Simske, S., Rejeb, K., Treiblmaier, H., & Zailani, S. (2020). Internet of Things research in supply chain management and logistics: A bibliometric analysis. Internet of Things, 12, 100318. [CrossRef]
• Savic, M., Lukic, M., Danilovic, D., Bodroski, Z., Bajović, D., Mezei, I., ... & Jakovetić, D. (2021). Deep Learning anomaly detection for cellular IoT with applications in smart logistics. IEEE Access, 9, 59406-59419. [CrossRef]
• Sharifani, K., & Amini, M. (2023). Machine learning and Deep Learning: A review of methods and applications. World Information Technology and Engineering Journal, 10(07), 3897-3904. [CrossRef]
• Shmueli, G. (2010). “To explain or to predict?”, Statistical Science, Vol. 25 No. 3, pp. 289-310. [CrossRef]
• Singh, A., Wiktorsson, M., & Hauge, J. B. (2021). Trends in machine learning to solve problems in logistics. Procedia CIRP, 103, 67-72. [CrossRef]
• Song, Y., Yu, F. R., Zhou, L., Yang, X., ve He, Z. (2021). Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey. IEEE Internet of Things Journal, 8(6), 4250-4274. [CrossRef]
• Speranza, G. M. (2018). Trends in transportation and logistics. European Journal of Operational Research, 264(3), 830–836. [CrossRef]
• Szpilko, D., & Ejdys, J. (2022). European Green Deal–research directions: A systematic literature review. Ekonomia i Środowisko, (2), 8-38. [CrossRef]
• Tamplin, M. L. (2018). Integrating predictive models and sensors to manage food stability in supply chains. Food microbiology, 75, 90-94. [CrossRef]
• Tran-Dang, H., Krommenacker, N., Charpentier, P., & Kim, D. S. (2022). The Internet of Things for logistics: Perspectives, application review, and challenges. IETE Technical Review, 39(1), 93-121. [CrossRef]
• Vikram, K., Siddipet, M. D., & Upadhayaya, N. (2011). Data mining tools and techniques: A review. Logistics management, 2(8), 31-39. [CrossRef]
• Woschank, M., Rauch, E., & Zsifkovits, H. (2020). A review of further directions for artificial intelligence, machine learning, and Deep Learning in smart logistics. Sustainability, 12(9), 3760. [CrossRef]
• Woschank, M., Rauch, E., & Zsifkovits, H. (2020). A review of further directions for artificial intelligence, machine learning, and Deep Learning in smart logistics. Sustainability, 12(9), 3760. [CrossRef]
• Yalçıntaş, D., Oğuz, S., Yaşa Özeltürkay, E., & Gülmez, M. (2023). Bibliometric analysis of studies on sustainable waste management. Sustainability, 15(2), 1414. [CrossRef]
• Yılmaz, Ü., & Kuvat, Ö. (2021). Nesnelerin İnterneti Teknolojisinin Lojistik Faaliyetlerindeki Uygulama Alanları ve Verimliliğe Etkileri. Avrupa Bilim ve Teknoloji Dergisi, (31), 746-754. [CrossRef]