Journals / Black Sea Journal of Engineering and Science / 2026 / Cilt: 9 Sayı: 3

AI-Supported Fleet and Network Design for a Hybrid Airline Model: A Case Study on an Airline Startup

Ahmet Ertek(KASTAMONU ÜNİVERSİTESİ, SİVİL HAVACILIK YÜKSEKOKULU, HAVACILIK YÖNETİMİ BÖLÜMÜ)0000-0002-8156-5075
Pages
1092–1100
Publication Date
2026-05-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The research results suggest that if an airline with a hybrid business model enters the sector, a balanced fleet with a mix of aircraft types could be utilized, while a structure centered in Istanbul, Ankara, and Izmir would provide strategic advantages.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The airline sector is constantly evolving in response to technological advancements, increasing competition among airlines, and changing passenger demands.
📊
Key Findings & Data:With these developments, strategic decisions such as fleet planning, demand forecasting, and flight network design are becoming key elements of sustainable growth and competitive advantage for airlines.
💡
Conclusion & Impact:The research results suggest that if an airline with a hybrid business model enters the sector, a balanced fleet with a mix of aircraft types could be utilized, while a structure centered in Istanbul, Ankara, and Izmir would provide strategic advantages.
🔬Design:Modeling & Simulation
📍Scope:2009-2024 Verileri
⚙️Variables:Artificial intelligence · Demand forecasting · Fleet planning

Abstract

The airline sector is constantly evolving in response to technological advancements, increasing competition among airlines, and changing passenger demands. With these developments, strategic decisions such as fleet planning, demand forecasting, and flight network design are becoming key elements of sustainable growth and competitive advantage for airlines. This study presents a conceptual AI-supported strategic planning framework that demonstrates the decision-making processes in which AI can be integrated into fleet planning, demand forecasting, and flight network design for a new airline with a hybrid business model in Türkiye. The study employed a qualitative research approach, and a conceptual case study was conducted within the conceptual framework to examine how AI applications can be integrated into decision-support processes. Within this framework, conceptual and strategic assessments were conducted by creating scenarios using examples from Turkish Airlines and Pegasus Airlines over a 15-year period spanning 2009-2024. Accordingly, the study includes airline business model design, demand forecasting, flight network design, schedule design, fleet design, and a SWOT analysis of the company. The research results suggest that if an airline with a hybrid business model enters the sector, a balanced fleet with a mix of aircraft types could be utilized, while a structure centered in Istanbul, Ankara, and Izmir would provide strategic advantages. In addition, the study reveals findings regarding the usability of artificial intelligence (AI) applications in airline operational processes and how they can be integrated. In this context, the study contributes to the literature on airline operations planning and offers practical recommendations for airlines.

Özet

The airline sector is constantly evolving in response to technological advancements, increasing competition among airlines, and changing passenger demands. With these developments, strategic decisions such as fleet planning, demand forecasting, and flight network design are becoming key elements of sustainable growth and competitive advantage for airlines. This study presents a conceptual AI-supported strategic planning framework that demonstrates the decision-making processes in which AI can be integrated into fleet planning, demand forecasting, and flight network design for a new airline with a hybrid business model in Türkiye. The study employed a qualitative research approach, and a conceptual case study was conducted within the conceptual framework to examine how AI applications can be integrated into decision-support processes. Within this framework, conceptual and strategic assessments were conducted by creating scenarios using examples from Turkish Airlines and Pegasus Airlines over a 15-year period spanning 2009-2024. Accordingly, the study includes airline business model design, demand forecasting, flight network design, schedule design, fleet design, and a SWOT analysis of the company. The research results suggest that if an airline with a hybrid business model enters the sector, a balanced fleet with a mix of aircraft types could be utilized, while a structure centered in Istanbul, Ankara, and Izmir would provide strategic advantages. In addition, the study reveals findings regarding the usability of artificial intelligence (AI) applications in airline operational processes and how they can be integrated. In this context, the study contributes to the literature on airline operations planning and offers practical recommendations for airlines.

🔖

Cite This Article

Ertek, A. (2026). AI-Supported Fleet and Network Design for a Hybrid Airline Model: A Case Study on an Airline Startup. Black Sea Journal of Engineering and Science, Cilt: 9 Sayı: 3, 1092–1100. https://doi.org/10.34248/bsengineering.1887710

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (25)

  1. Aydıner, Ö. Ş. (2019). Optimizing airline operations under uncertainty (Doctoral dissertation). Bilkent University, Institute of Engineering and Science.
  2. Bieger, T., & Agosti, S. (2005). Business models in the airline sector – evolution and perspectives. In W. Delfmann, H. Baum, S. Auerbach and S. Albers (Eds.), Strategic management in the aviation industry. Aldershot: Ashgate Publishing Group.
  3. Bieger, T., Döring, T., & Laesser, C. (2002). Transformation of business models in the airline industry - impact on tourism. Air Transport and Tourism, 52nd Congress, Brasil.
  4. Bolat, A. E. (2019). A Mathematical Programming Model for Aircraft Fleet Management. PhD Thesis, Yıldız Technical University, Institute of Science, Istanbul.
  5. Cansino, J. M. & Romána R. (2017). Energy Efficiency Improvements in Air Traffic: The Case of Airbus A320 in Spain. Energy Policy, 101: 109-122.
  6. Clark, P. (2007). Buying the Big Jets: Fleet planning for Airlines. Farnham: Ashgate Publishing.
  7. Dursun, Ö. O., & Aksoy, C. (2017). Environmental Impact of Airports. Academic Social Research Journal, (53): 361-371.
  8. Gümüş Akar, P., Manga, M., & Bal H. (2019). The Causal Relationship Between Air Transportation Liberalization and Economic Growth: The Case of Turkey. Gaziantep University Journal of Social Sciences, 18(3): 1160-1174.
  9. Hvass, K. (2012). A Boolean Approach to Airline Business Model Innovation. http://openarchive.cbs.dk/xmlui/bitstream/handle/10398/8403/Kristian_Hvass_ WP_2012.pdf? sequence=1
  10. Korul, V. (2003). Airport Environmental Management System. Social Sciences Journal, 99-120.
  11. Kumar, V., Dinesh Kumar K., & Pavan Kumar C. (2012). System and Method for Aircraft Performance Predictions for Descent and Approach Phases, PCT/US20120277969A1. Bangalore: Airbus Engineering Centre.
  12. Merkert, R., & Hensher D. A. (2011). The Impact of Strategic Management and Fleet Planning on Airline Efficiency – A Random Effects Tobit Model Based on DEA Efficiency Scores. Transportation Research Part A, 45 (7): 686-695.
  13. Özger, A., & Oktal, H. (2013). A new approach to single allocation hub location problem and an application in the air cargo transportation. Pamukkale University Journal of Engineering Sciences, 19(2): 68-75. https://doi.org/10.5505/pajes.2013.43043
  14. Pegasus Airlines. (2025). Pegasus Airlines Joint Stock Company Annual Activity Report of the Board of Directors for the Period January 1 – December 31, 2024.
  15. Sadou, A. M., & Tchouamou Njoya, E. (2023). Applications of artificial intelligence in the air transport industry: A bibliometric and systematic literature review. Journal of Aerospace Technology and Management, 15, e2223. https://doi.org/10.1590/jatm.v15.1312
  16. Snow, J. (2004). Fleet Planning Workshop June, Cranfield University.
  17. Srisaeng, P., & Baxter, G. (2022). Application of an ANFIS to estimate Kansai International Airport’s international air passenger demand. Journal of Aviation, 6(1), 87–92. https://doi.org/10.30518/jav.1062151
  18. Taneja, N. K. (2004). Simpli-Flying: optimizing the airline business model. Ashgate Pub Limited.
  19. Taşçı, D., & Yalçınkaya, A. (2015). A New Business Model in The Airline Industry: Airline Within Airline Model and A Comparison Through the case of AnadolujeT. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 10 (2): 177-204.
  20. Taylor, J.W.R. (2005). Jane’s All the World Aircraft, Jane’s Publishing Company. (2004-2005).
  21. Turkish Airlines. (2025). January–December 2024 traffic results. Turkish Airlines Investor Relations.
  22. Vidovic, A., Stimac, I., & Vince, D. (2013). Development of Business Models of Low-Cost Airlines. International Journal for Traffic and Transport Engineering, 3(1).
  23. Yılmaz, S. (2006). Application of AHP and fuzzy AHP to aircraft selection criteria (Master's thesis, Yıldız Technical University, Institute of Science). Higher Education Council National Thesis Center.
  24. Zachariah, R. A., Sharma, S., & Kumar, V. (2023). Systematic review of passenger demand forecasting in aviation industry. Multimedia Tools and Applications. Advance online publication. https://doi.org/10.1007/s11042-023-15552-1
  25. Zaoui, A., Tchuente, D., Fosso Wamba, S., & Kamsu-Foguem, B. (2024). Impact of artificial intelligence on aeronautics: An industry-wide review. Journal of Engineering and Technology Management, 71, 101800. https://doi.org/10.1016/j.jengtecman.2024.101800