Research Article
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Investigation of Machine Learning Applications in Commercial Air Transportation Industry

Year 2019, Volume: 22 Issue: 2, 405 - 419, 30.11.2019
https://doi.org/10.29249/selcuksbmyd.537142

Abstract

It is very important that the airlines that carry out operations
in a dynamic and complex environment struggle to make the right decision
despite many limitations. Today, a wide range of data and a large amount of
data generated by airline companies and their ability of data evaluation will
determine the effectiveness of the decisions. For this reason, in this study,
it has been tried to determine the applications of Artificial Intelligence (AI)
and Machine Learning (ML) algorithms in airline processes of by examining
previous literature. The results show that there has been an increase in the
application ML algorithms in “dispatch reliability”, “flight safety”, “yield
management/pricing” and “customer behavior” issues especially in recent years.

References

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Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi

Year 2019, Volume: 22 Issue: 2, 405 - 419, 30.11.2019
https://doi.org/10.29249/selcuksbmyd.537142

Abstract

Karmaşıklığın oldukça fazla olduğu, dinamik bir çevrede
operasyonlarını sürdüren havayolu işletmelerinin birçok kısıta rağmen doğru
karar almaları oldukça önemlidir. Bugün çok çeşitli veri ve büyük miktarda veri
üreten havayolu işletmelerinin bu verileri en doğru şekilde değerlendirebilme
becerileri kararlarının etkinlik derecesini belirleyebilecektir. Bu nedenle, bu
çalışma kapsamında Yapay Zeka (YZ) uygulaması olan Makine Öğrenmesinin (MÖ)
havayolu işletmelerinin hangi süreçlerinde, hangi algoritmalar ile
kullanılabileceği alanyazında yer alan çalışmalar incelenerek tespit edilmeye
çalışılmıştır. Elde edilen sonuçlar, özellikle son yıllarda MÖ’nün “dispeç
güvenilirliği”, “uçuş emniyeti”, “gelir yönetimi/fiyatlama” ve “müşteri davranışları”
konularına uygulanmasında bir artış olduğunu ortaya koymaktadır.
azarlarına aittir.

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  • Andronie, M. (2015). Airline Applications of Business Intelligence Systems. INCAS, 7(3), 153-160.
  • Aoun, O., Sarhani, M., & El Afia, A. (2016). Investigation of hidden markov model for the tuning of metaheuristics in airline scheduling problems. IFAC PapersOnLine., 49, 347-352.
  • Atalay, M., & Çelik, E. (2017). Büyük Veri Analizinde Yapay Zekâ ve Makine Öğrenmesi Uygulamaları. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(22), 155-172.
  • Aytug, H., Bhattacharyya, S., Koehler, G., & Snowdon, J. (1994). A review of machine learning in scheduling. IEEE Transactions on Engineering Management, 41(2), 165-171.
  • Azoff, M. (2015). Machine Learning in Business Use Cases:Artificial intelligence solutions that can be applied today. London: OVUM.
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  • Barnhart, C., Farahat, A., & Lohatepanont, M. (2009). Airline Fleet Assignment with Enhanced Revenue Modeling. Barnhart, C.,
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  • Bartosz, B., Dariusz, M., & Krzysztof, A. C. (2018). A Machine Learning Approach to the Detection of Pilot’s Reaction to Unexpected Events Based on EEG Signals. Computational Intelligence and Neuroscience, 2, 1-9.
  • Bayoumi, A. E.-M., Saleh, M., Atiya, A., & Aziz, H. A. (2013). Dynamic Pricing for Hotel Revenue Management Using Price Multipliers. Journal of Revenue and Pricing Management, 12(3), 271-285.Belkin, V. A. (2017). On the Issue of Aircraft Maitenance Process Optimization on the Criterion of Minimum Fuel Consumption. Naučnyj Vestnik MGTU GA, 20(1), 61-68.
  • Belobaba, P. (2016). Airline Operating Costs and Measures of Productivity. P. Belobaba, A. Odoni, & C. Barnhart içinde, The Global Airline Industry (s. 146-158). Noida, India: John Wiley & Sons.
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  • Belobaba, P. P., & Farkas, A. (1999). Yield management impacts on airline spill estimation. Transportation Science, 33(2), 217–232.
  • Belobaba, P., Odoni, A., & Barnhart, C. (2009). The Global Airline Industry. Wiltshire: John Wiley & Sons.
  • Bhatnagar, R. (2018). Machine Learning and Big Data Processing: A Technological Perspective and Review. M. F. Aboul Ella Hassanien içinde, The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (s. 468–478). Cham: Springer.
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  • Bude, G., Hoogenboom, L., Kastrop, W., Reniers, N., & Frasincar, F. (2018). Predicting User Flight Preferences in an Airline E-Shop. Web Engineering, 2018, 245-260.
  • Caetano, D. J., Dionisio, N., & Gualda, F. (2017). Daniel JorgAn exact model for airline flight network optimization based on transport momentum and aircraft load factor. Daniel Jorge Caetano, Nicolau Dionisio Fares Gualda. An exact model for airline flight network optimizatioTransportes, 25(4), 14-26.
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Details

Primary Language Turkish
Journal Section Original Research Articles
Authors

Eyüp Bayram Şekerli 0000-0003-1562-4716

Publication Date November 30, 2019
Submission Date March 7, 2019
Published in Issue Year 2019 Volume: 22 Issue: 2

Cite

APA Şekerli, E. B. (2019). Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 22(2), 405-419. https://doi.org/10.29249/selcuksbmyd.537142

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