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Investigation of Machine Learning Applications in Commercial Air Transportation Industry

Yıl 2019, Cilt: 22 Sayı: 2, 405 - 419, 30.11.2019
https://doi.org/10.29249/selcuksbmyd.537142

Öz

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.

Kaynakça

  • Abhijit, G. (2004). A Reinforcement Learning Algorithm Based on Policy Iteration for Average Reward: Empirical Results with Yield Management and Convergence Analysis. Machine Learning, 55(1), 5-29.
  • Achenbach, A., & Spinler, S. (2018). Prescriptive Analytics in Airline Operations: Arrival time prediction and cost index optimization for short-haul flights. Operations Research Perspectives, 5, 265-279.
  • Al-Tabbakh, S. M., Mohamed, H. M., & El-Zahed, H. (2018). Machine Learning Techniques For Analysis Of Egyptian Flight Delay. International Journal of Data Mining & Knowledge Management Process (IJDKP), 8(3), 1-14.
  • Amnur, H. (2017). Customer Relationship Management and Machine Learning. International Journal of Informatics Visualization, 1(1), 12-15.
  • 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.
  • Badea, L. M. (2014). Predicting Consumer Behavior with Artificial Neural Networks. Procedia Economics and Finance, 15, 238-246.
  • Bai, Y., Sun, Z., Deng, J., Li, L., Long, J., & Li, C. (2018). Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study,. Sustainability, 10(1), 1-15.
  • Balakrishna, P., Ganesan, R., & Sherry, L. A. (2010). Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: A case-study of Tampa Bay departures. Transportation Research: Part C., 18(6), Balakrishna P, Ganesan R, Sherry L. Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: A case-s950-962.
  • Barnhart, C., & Cohn., A. (2004). Airline Schedule Planning: Accomplishments and Opportunities. Manufacturing and Service Operations Management, 6(1), 3-22.
  • Barnhart, C., Farahat, A., & Lohatepanont, M. (2009). Airline Fleet Assignment with Enhanced Revenue Modeling. Barnhart, C.,
  • Farahat, A., & Lohatepanont, M. (2009). AiOperations Research, 57(1), 231–244.
  • 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.
  • Belobaba, P. P. (1987). Airline Yield Management. An Overview of Seat Inventory Control. Transportation Science, 21(2), 63-73.
  • 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.
  • Boelaert, J., & Ollion, É. (2018). The Great Regression. Revue française de sociologie, 3(59), 216-246.
  • Bramer, L. M., Chatterjee, S., Holmes, A. E., Robinson, S. M., Bradley, S. F., & Webb-Robertson, B.-J. M. (2015). A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data. The 11th International Conference on Data Mining (DMIN 2015) (s. 162-167). Las Vegas, Nevada: Pacific Northwest National Lab. .
  • Buchanan, B. (2005). A (very) Brief History of Artificial Intelligence. AI Magazine, Winter, 53-60.
  • 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.
  • Chiu, C., Chiu, N. H., & Hsu, C. I. (2004). Intelligent aircraft maintenance support system using genetic algorithms and case-based reasoning. International Journal of Advanced Manufacturing Technology, 24(5), Chiu, C & Chiu, N.-H & Hsu, C.-I. (2004). Intelligent aircraft maintenance support system using genetic algorithms and case-ba440-446.
  • Cioca, M., Ghete, A. I., Cioca, L.-I., & Gîfu, D. (2013). Machine Learning and Creative Methods Used to Classify Customers in a CRM Systems. Applied Mechanics and Materials, 317, 769-773.
  • Collins, A., & Thomas, L. (2012). Comparing reinforcement learning approaches for solving game theoretic models: a dynamic airline pricing game example. A Collins, L Thomas. Comparing reinforcement learning approaches for solving game theoretic models: aThe Journal of the Operational Research Society, 63(8), 1165-1173.
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Ticari Havayolu Taşımacılığı Sektöründe Makine Öğrenmesi Uygulamalarının İncelenmesi

Yıl 2019, Cilt: 22 Sayı: 2, 405 - 419, 30.11.2019
https://doi.org/10.29249/selcuksbmyd.537142

Öz

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.

Kaynakça

  • Abhijit, G. (2004). A Reinforcement Learning Algorithm Based on Policy Iteration for Average Reward: Empirical Results with Yield Management and Convergence Analysis. Machine Learning, 55(1), 5-29.
  • Achenbach, A., & Spinler, S. (2018). Prescriptive Analytics in Airline Operations: Arrival time prediction and cost index optimization for short-haul flights. Operations Research Perspectives, 5, 265-279.
  • Al-Tabbakh, S. M., Mohamed, H. M., & El-Zahed, H. (2018). Machine Learning Techniques For Analysis Of Egyptian Flight Delay. International Journal of Data Mining & Knowledge Management Process (IJDKP), 8(3), 1-14.
  • Amnur, H. (2017). Customer Relationship Management and Machine Learning. International Journal of Informatics Visualization, 1(1), 12-15.
  • 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.
  • Badea, L. M. (2014). Predicting Consumer Behavior with Artificial Neural Networks. Procedia Economics and Finance, 15, 238-246.
  • Bai, Y., Sun, Z., Deng, J., Li, L., Long, J., & Li, C. (2018). Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study,. Sustainability, 10(1), 1-15.
  • Balakrishna, P., Ganesan, R., & Sherry, L. A. (2010). Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: A case-study of Tampa Bay departures. Transportation Research: Part C., 18(6), Balakrishna P, Ganesan R, Sherry L. Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: A case-s950-962.
  • Barnhart, C., & Cohn., A. (2004). Airline Schedule Planning: Accomplishments and Opportunities. Manufacturing and Service Operations Management, 6(1), 3-22.
  • Barnhart, C., Farahat, A., & Lohatepanont, M. (2009). Airline Fleet Assignment with Enhanced Revenue Modeling. Barnhart, C.,
  • Farahat, A., & Lohatepanont, M. (2009). AiOperations Research, 57(1), 231–244.
  • 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.
  • Belobaba, P. P. (1987). Airline Yield Management. An Overview of Seat Inventory Control. Transportation Science, 21(2), 63-73.
  • 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.
  • Boelaert, J., & Ollion, É. (2018). The Great Regression. Revue française de sociologie, 3(59), 216-246.
  • Bramer, L. M., Chatterjee, S., Holmes, A. E., Robinson, S. M., Bradley, S. F., & Webb-Robertson, B.-J. M. (2015). A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data. The 11th International Conference on Data Mining (DMIN 2015) (s. 162-167). Las Vegas, Nevada: Pacific Northwest National Lab. .
  • Buchanan, B. (2005). A (very) Brief History of Artificial Intelligence. AI Magazine, Winter, 53-60.
  • 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.
  • Chiu, C., Chiu, N. H., & Hsu, C. I. (2004). Intelligent aircraft maintenance support system using genetic algorithms and case-based reasoning. International Journal of Advanced Manufacturing Technology, 24(5), Chiu, C & Chiu, N.-H & Hsu, C.-I. (2004). Intelligent aircraft maintenance support system using genetic algorithms and case-ba440-446.
  • Cioca, M., Ghete, A. I., Cioca, L.-I., & Gîfu, D. (2013). Machine Learning and Creative Methods Used to Classify Customers in a CRM Systems. Applied Mechanics and Materials, 317, 769-773.
  • Collins, A., & Thomas, L. (2012). Comparing reinforcement learning approaches for solving game theoretic models: a dynamic airline pricing game example. A Collins, L Thomas. Comparing reinforcement learning approaches for solving game theoretic models: aThe Journal of the Operational Research Society, 63(8), 1165-1173.
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  • Lheritier, A., Bocamazo, M., Delahaye, T., & Acuna-Agost, R. (2018). Airline itinerary choice modeling using machine learning. Journal of Choice Modelling, DOI: 10.1016/j.jocm.2018.02.002. ´.
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  • Lu, Z., Liang, X. Z., & Zhou, J. (2017). Markov process based time limited dispatch analysis with constraints of both dispatch reliability and average safety levels. Reliability Engineering & System Safety, 167, 84-94.
  • M., S., Deokar A., V., & Janze, C. (2018). Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews. Decision Support Systems, 107, 52-63.
  • Mei, A. v., & Doomernik, J.-P. (2017). Artificial intelligence potential in power distribution system planning. CIRED - Open Access Proceedings Journal, 2017, 2115-2117.
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  • Ming, W., Bao, Y., Hu, Z., & Xiong, T. (2014). Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models. The Scientific World Journal, 2014, 1-14.
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Toplam 101 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makalesi
Yazarlar

Eyüp Bayram Şekerli 0000-0003-1562-4716

Yayımlanma Tarihi 30 Kasım 2019
Gönderilme Tarihi 7 Mart 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 22 Sayı: 2

Kaynak Göster

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

Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.