Using Artificial Intelligence and Machine Learning Applications in Logistics

Year 2021, Volume: 8 Issue: 1, 74 - 93, 31.01.2021

Batin Latif Aylak , Okan Oral , Kübra Yazıcı

https://doi.org/10.31202/ecjse.776314

Cited By: 16

Abstract

The logistics sector in Turkey and the world is growing and the sector's potential is better understood over time. It is known that the logistics sector is very open to development and has to keep up with the innovations that occur with technology. Businesses are trying to be successful in competition by keeping up with these innovations. Industry 4.0 has influenced the sectors where competition is at the forefront, especially logistics. In recent studies, it has been observed that a significant increase in the use of artificial intelligence techniques. As a result of the use of artificial intelligence in the logistics sector, changes in operations and dynamics have started to occur. Artificial intelligence models the physiological and neurological structure of human intelligence with the help of various technologies and transfers them to machines. Options such as driverless vehicles emerging with artificial intelligence, robots used in storage and shelves, and the easy use of big data in the system ensure that the errors in the logistics sector are minimized and convenience is provided in this way. Thanks to the use of artificial intelligence in the logistics sector, businesses create more efficient jobs. In this study, it is aimed to examine the artificial intelligence and machine learning applications used in the logistics industry with a broad perspective. In the study, firstly, the concepts of artificial intelligence and machine learning are explained and then, the concepts of industry and logistics are mentioned and the applications of artificial intelligence and machine learning used in logistics are included. It is seen that artificial intelligence improves day by day and facilitates logistics processes in global logistics and supply chain management.

Keywords

Artificial intelligence, Machine learning, Logistics

Yapay Zeka ve Makine Öğrenmesi Tekniklerinin Lojistik Sektöründe Kullanımı

Abstract

Lojistik sektörü dünyada ve Türkiye’de giderek büyümekte ve sektörün potansiyeli zamanla daha iyi anlaşılmaktadır. Lojistik sektörünün gelişime oldukça açık olduğu, teknoloji ile ortaya çıkan yeniliklere ayak uydurmak zorunda olduğu bilinmektedir. İşletmeler bu yeniliklere ayak uydurarak, rekabette başarılı olmaya çalışmaktadır. Endüstri 4.0 özellikle lojistik gibi rekabetin ön planda olduğu sektörleri etkisi altına almıştır. Yapılan son araştırmalarda yapay zeka tekniklerinin kullanımında büyük oranda artış olduğu görülmektedir. Yapay zekanın lojistik sektöründe kullanılması sonucunda özellikle işleyiş ve dinamiklerde değişiklikler oluşmaya başlamıştır. Yapay zeka, insan zekasının fizyolojik ve nörolojik yapısını çeşitli teknolojiler yardımı ile modelleyerek makinelere aktarmaktadır. Yapay zeka ile birlikte ortaya çıkan sürücüsüz araçlar, depolama ve raflarda kullanılan robotlar, sistem içerisinde büyük verilerin rahatlıkla kullanılması gibi seçenekler lojistik sektöründeki hataların en aza indirgenmesini sağlamaktadır. Lojistik sektöründe yapay zeka kullanımı sayesinde işletmeler daha verimli işler ortaya koymaktadır. Yapılan bu çalışmada, lojistik sektöründe kullanılan yapay zeka ve makine öğrenmesi uygulamalarının geniş bir perspektif ile incelenmesi amaçlanmıştır. Çalışmada önce yapay zeka ve makine öğrenimi kavramları açıklanmış ardından endüstri ve lojistik kavramlarına değinilerek lojistikte kullanılan yapay zeka ve makine öğrenmesi uygulamalarına yer verilmiştir. Küresel lojistik ve tedarik zinciri yönetimi konusunda yapay zekanın günden güne kendini geliştirdiği ve lojistik süreçleri kolaylaştırdığı görülmektedir.

Keywords

Yapay zeka, Makine öğrenmesi, Lojistik

References

• Baki, B., “Lojistik Yönetimi ve Lojistik Sektör Analizi”, 1. Baskı, Lega Kitabevi, 9758714023, Trabzon, 2004.
• Barreto, L., Amaral A. and Teresa P., “Industry 4.0 Implications in Logistics: An Overview”, Procedia Manufacturing, 2017, 13, 1245-1252.
• Schlaepfer, R. C., Koch, M. and Merkofer, P. “Industry 4.0 Challenges and Solutions for the Digital Transformation and Use of Exponential Technologies”, Deloitte AG Research Report, Zurich. 2015.
• Scherf, J., “Was ist Logistik 4.0? Alles zum Thema Digitalisierung & Logistik”, 2018, Erişim adresi: https://www.mmlogistik.vogel.de/was-ist-logistik-40-alles-zum-thema-digitalisierung-logistik-a-692722 Erişim tarihi: 17.07.2020
• Schiemann, J., “Logistics 4.0 - How Autonomous are Self-managed Processes?”, Axit Connecting Logistics, 2016, 16.
• Özdemir, A., Özgüner, M., “Endüstri 4.0 ve Lojistik Sektörüne Etkileri: Lojistik 4.0.”, İşletme ve İktisat Çalışmaları Dergisi. 2018, 6 (4), 39-47 ISSN:2147-804X.
• Ertemel, A. V., Alış, G. ve Pirtini, S., “Lojistik Sektöründe Endüstri 4.0 Uygulamalarının Operasyonel Verimliliğe Etkisi”, Business and Management Studies An International Journal. 2020, 8 (1), 371-395.
• Min, H., “Artificial Intelligence in Supply Chain Management: Theory And Applications”, International Journal of Logistics: Research And Applications. 2010, 13 (1), 13-39.
• Demirhan, A., Kılıç, Y.A. ve Güler, İ., “Tıpta Yapay Zeka Uygulamaları”, Yoğun Bakım Dergisi 2010, 9 (1), 31-41.
• Atalay, M., Çelik, E., “Büyük Veri Analizinde Yapay Zekâ ve Makine Öğrenmesi”, Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enst. Dergisi, 2017, 9 (22), 155-172.
• Nabiyev, V. V., “Yapay Zekâ: İnsan-Bilgisayar Etkileşimi”, Seçkin Yayıncılık, 2012.
• Andrew, A. M., “Artificial Intelligence”, Boston: AddisonWesley Company, 1991.
• Popov, E. V., (Ed), “Yapay Zekâ”, Uzman Sitemler ve Doğal Dil İşleme. Moskova: Radio i Svyaz, 1990, 461.
• Copeland, J. “Artificial Intelligence: A Philosophical”, Blackwell: Oxford, 1993.
• Charniak, E., McDermot, D., “Introduction to Artifical Intelligence”, Boston: Addison-Wesley Company, 1985.
• Güngör, D. N., Yardımcı, U, İ., “Yapay Zeka Kavramı ve Makine Öğrenme Uygulamaları”, Erişim adresi: https://stratejico.com/yapay-zeka-kavrami-ve-makine-ogrenme-uygulamalari Erişim Tarihi: 27.07.2020
• Intellipaah., “What is Artificial Intelligence?”, Erişim adresi: https://intellipaat.com/blog/ what-is-artificial-intelligence/ Erişim tarihi:27.07.2020
• Demirkan, H., Earley, S. and Harmon, R. R., “Cognitive Computing”, IT Professional, 2017, 19 (4), 16-20.
• Davies, E., “Machine Vision, third ed.”, Signal Processing and its Applications, Morgan Kaufmann, Burlington, 2005. doi:https://doi.org/10.1016/B978-0-12-206093-9.
• Ratner, B., “A comparison of two popular machine learning methods”, DM STAT-1 Online Newsletter about Quantitative Methods in Direct Marketing, 2000, 4.
• McCulloch, W. S., Pitts, W., “A logical calculus of the idea imminent in nervous activity”, Bulletin of Mathematical Biophysics, 1943, 5, 115-137.
• Russell, S., Norvig, P., “Artificial intelligence: a modern approach”, Upper Saddle River, NJ: Prentice-Hall Samuel, A. L. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 1995, 3 (3), 210-229.
• Öztemel, E., “Yapay Sinir Ağları”, Papatya Yayıncılık, İstanbul, 2003.
• Çakıroğlu, M. A., Süzen A. A., “Assessment and Application of Deep Learning Algorithms in Civil Engineering”, El-Cezerî Fen ve Mühendislik Dergisi 2020, 7(2), 906-922.
• LeCun, Y., Bengio, Y. and Hinton, G., “Deep learning”. Nature, 2015, 521, 436-444.
• Adalı, E., “Doğal Dil İşleme”, Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 2016, 5 (2). https://dergipark.org.tr/tr/pub/tbbmd/issue/22245 /238797
• Samuel, A. L., “Some Studies in Machine Learning Using the Game of Checkers”, IBM Journal of Research and Development, 1959, 3(3), 210-229.
• Domingos, P., “A few useful things to know about machine learning”, Communications of the ACM, 2012, 55 (10), 78-87.
• Hannah, W., Daniel, K. and Saskia, S., “A Literature Review on Machine Learning in Supply Chain Management”, International Conference of Logistics (HICL), 2019, 27, 413-441, ISBN 978-3-7502-4947-9.
• Marsland, S., “Machine learning”, An algorithmic perspective. Boca Raton, London, 2015.
• Russell, S. J., Norvig, P., “Artificial Intelligence”, A modern approach. Boston, Columbus, Indianapolis, 2016.
• Hastie, T., Friedman, J. and Tibshirani, R., “The Elements of Statistical Learning”, New York, NY:Springer, New York, 2017.
• Gentsch, P., “Künstliche Intelligenz für Sales, Marketing und Service”, Mit AI und Bots einem Algorithmic Business - Konzepte, Technologien Und Best Practices, 2018. Erişim adresi: https://link.springer.com/content/ pdf/10.1007%2F978-3-658-19147-4.pdf. Erişim tarihi: 24.04.2020
• Belantová, T., Gálová, K., and Taraba, P., “Logistics Projects in the Czech Republic”, Transportation Research Procedia, 2019, 40, 949-954.
• Amr, M., Ezzat, M., and Kassem, S., “Logistics 4.0: Definition and Historical Background”, NILES 2019 - Novel Intelligent and Leading Emerging Sciences Conference, 46-49. https://doi.org/10.1109/NILES.2019.8909314.
• Balasubramani, P., Puranik, S.M., Rao, S.V., Hegde, A. S., “Analysis of Customer Churn prediction in Logistic Industry using Machine Learning”, International Journal of Scientific and Research Publications, 2017, 7(11), ISSN2250-3153.
• Chen, K., Hu, Y. and Hsieh, Y., “Predicting customer churn from valuable B2B customers in the logistics industry: a case study”, Information Systems and e-Business Management, 2015, 13, 475-494.
• JDA Software, “Erfolgreicher Einsatz von Bedarfsprognosen bei dm”, Wie künstliche Intelligenz bei der Drogeriemarktkette Mehrwert schafft, 2019. Erişim tarihi : 26.04.2020.
• Wenzel, H., Smit, D. and Sardesai, S., “A literature review on machine learning in supply chain management, Innovative Approaches for Supply Chains”, Proceedings of the Hamburg International Conference of Logistics (HICL), Berlin, 2019. 27, 413-441.
• Talupula, A., “Demand Forecasting of Outbound Logistics Using Machine learning”, Yüksek Lisans Tezi. Blekinge Institute of Technology, Karlskrona, Sweden, 2018.
• Merkuryeva, G., Valberga, A.; Smirnov, A. “Demand forecasting in pharmaceutical supply chains: A case study”, Procedia Comput. Sci. 2019, 149, 3-10.
• Inprasit, T., Tanachutiwat, S., “Reordering Point Determination Using Machine Learning Technique for Inventory Management”, International Conference on Engineering, Applied Sciences and Technology (ICEAST), 2018. DOI: 10.1109/ICEAST.2018.8434473.
• Qopius., “Bilgisayarla görme örneği”, Erişim adresi: https://www.qopius.com/ technology, Erişim Tarihi: 16.07.2020
• Dosdoğru, A. T., İpek, A. B., ve Göçken, M., “A novel hybrid artificial intelligence-based decision support framework to predict lead time”, International Journal of Logistics Research and Applications, DOI: 10.1080/13675567, 2020, 1749249.
• Lee, C. K. M., Ho, W., Ho, G. T. S., and Lau, H. C. W., “Design and development of logistics workflow systems for demand management with RFID”, Expert Systems with Applications, 2011, 38, 5428-5437.
• Ma, H., Wang, Y. and Wang, K., “Automatic detection of false positive RFID readings using machine learning algorithms”, Expert Systems With Applications , 2018, 91, 442-451.
• Tavana, M., Fallahpour, A., Caprio, D.D., Santos-Arteaga, F. J., “A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection”, Expert Systems With Applications, 2016, 61, 129-144.
• Rashidi, K., Cullinane, K., “A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: implications for sourcing strategy”, Expert Systems With Applications, 2019, 121, 266-281.
• Becker, T., Illigen, C., McKelvey, B., Hülsmann, M., Windt, K., “Using an agent-based neural-network computational model to improve product routing in a logistics facility”, Int J Prod Econ, 2016, 174,156-167.
• Li, Y., Lim, M. K. and Tseng, M.L., “A green vehicle routing model based on modified particle swarm optimization for cold chain logistics”, Industrial Management and Data Systems, 2019, 119 (3), 473-494.
• Azadeh, A., Farrokhi-Asl, H., “The close–open mixed multi depot vehicle routing problem considering internal and external fleet of vehicles”, Transportation Letters, 2019, 11(2), 78-92, DOI: 10.1080/19427867.2016.1274468.
• Holland, C., Levis, J., Nuggehalli, R., Santilli, B., Winters, J., “UPS Optimizes Delivery Routes”, Interfaces, 2017, 47(1), 8-23. https://doi.org/10.1287/inte.2016.0875.
• Lang, S., Schenk, M. and Reggelin, T., “Towards Learning and Knowledge Based Methods of Artificial Intelligence for Short Term Operative Planning Tasks in Production and Logistics: Research Idea and Framework”, IFAC PapersOnLine, 2019, 52(13), 2716-2721.
• Govindan, K., Cheng, T. C. E., Mishra, N., Shukla, N., “Big data analytics and application for logistics and supply chain management”, Transp. Res. Pt. e-Logist. Transp. Rev., 2018, 114, 343-349.
• DHL, “Artificial Intelligence in Logistics”, 2018. Erişim adresi: https://www.dhl.com/global-en/home/insights-and-innovation/insights/artificialintelligence. html Erişim tarihi:15.07.2020.
• Borusan Lojistik, Erişim Adresi: https://www.borusanlojistik.com/tr Erişim tarihi: 20.10.2020.
• Jiang, J., Wang, H., Mu, X., Guan, S., “Logistics industry monitoring system based on wireless sensor network platform”, Computer Communications, 2020, 155, 58-65.
• Borstell, H., Reggelin, T., “Towards Virtual Commissioning of Image-based Information Systems for State Detection in Logistics”, IFAC PapersOnLine, 2019, 52(13), 2463-2470.
• Adıgüzel Tüylü, A. N., Eroğlu, E., “Using Machine Learning Algorithms For Forecasting Rate of Return Product In Reverse Logistics Process”, Alphanumeric Journal, 2019, 7(1), 143-156. http://dx.doi.org/10.17093/alphanumeric.541307).
• Robinson, A., “The Top 5 Changes That Occur with AI in Logistics”, 2020. Erişim adresi: https://cerasis.com/ai-in-logistics/ Erişim tarihi: 15.07.2020.
• Fizyr sipariş toplama uygulamaları, Erişim adresi https://fizyr.com/ Erişim Tarihi: 17.07.2020
• Choy, K. L., Ho, G. T. S. and Lee, C. K. H., “A RFID-Based Storage Assignment System for Enhancing the Efficiency of Order Picking”, Journal of Intelligent Manufacturing, 2017, 28(1),111-129.
• Villarreal-Zapata, G., Salais-Fierro, T. E. and Saucedo-Martínez, J. A. “Intelligent system for selection of order picking Technologies”, Wireless Netw, 2020. https://doi.org/10.1007/s11276-020-02262-x.
• Geigl, F., Moik, C., Hintereggerz, S., Goller, M., “Using machine learning and RFID localization for advanced logistic applications”, IEEE International Conference on RFID (RFID), 2017, 73-74.
• Su, T., Hwang, M., “An efficient order-picking route planning based on a fuzzy set method with a multiple-aisle in a distribution center”. 27th International Conference on Flexible Automation and Intelligent Manufacturing, 2017, 1856-1862.
• DHL, “Resilience 360 Supply Watch platformu”, Erişim adresi: https://www.dhl.com/global-en/home/press/press-archive/2017/dhl-supply-watch-machine-learning-to-mitigate-supplier-risks.html Erişim tarihi: 16.07.2020.
• IBM, “Watson Visual Recognition: Maintenance with AI-driven Visual Inspection”, 2018. Erişim Adresi: https://www.ibm.com/topics/computer-vision Erişim tarihi: 15.07.2020.
• Dale M., “Automating grocery shopping”, Imaging and Machine Vision Europe, 85, 2018, 16-20.
• Toh, C. K., Sanguesa, J. A., Cano, J. C., Martinez, F. J., “Advances in smart roads for future Smart Cities”, Proceedings of the Royal Society A, 2020, 476(2233), 20190439.
• Lojistiğin Kaderini Değiştiren Teknoloji: Yapay Zeka, Erişim adresi: https://www.globelink-unimar.com/lojistigin-kaderini-degistiren-teknoloji-yapay-zeka Erişim tarihi: 19.07.2020
• Hua, H., Zhang, Z., “Application of Artificial Intelligence Technology in Short-range Logistics Drones”, 8th International Symposium on Next Generation Electronics (ISNE), Zhengzhou, China, 2019.
• Amazon tarafından kullanılan teslimat aracı, Erişim adresi: https://www.amazon.com/ Amazon-Prime-Air/b?ie=UTF8&node= 8037720011 Erişim tarihi: 19.07.2020
• Bilgin Sarı, E., Özveri, O. ve Şenyay, U. E., “Endüstri 4.0’ın İş Süreçleri Yönetimine Etkisi: Akıllı Depolama Sistemi Uygulaması”, Uluslararası Yönetim Akademisi Dergisi, 2019, 2(2), 466-477.