Journals / Endüstri Mühendisliği / 2026 / Cilt: 37 Sayı: 1

OPTIMIZING FUEL-EFFICIENT TRAJECTORY RECOVERY AFTER AIRCRAFT CONFLICT-AVOIDANCE MANEUVERS: A DECISION SUPPORT APPROACH

UÇAKLARIN ÇAKIŞMA ÖNLEYİCİ MANEVRALARI SONRASI YAKIT TÜKETİMİNİ ENAZLAYACAK ŞEKİLDE YÖRÜNGEYE GERİ DÖNMELERİNİ SAĞLAYACAK KARAR DESTEK SİSTEMİ ÖNERİSİ

Elifnur Aktürk(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ, MÜHENDİSLİK-MİMARLIK FAKÜLTESİ)0009-0002-8111-0676
Beyza Aldanmaz(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ, MÜHENDİSLİK-MİMARLIK FAKÜLTESİ)0009-0002-9633-6416
Şevin Tuğcu(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ, MÜHENDİSLİK-MİMARLIK FAKÜLTESİ)0009-0006-1193-2120
Müjgan Sağır(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0003-2781-658X
Pages
151–178
Publication Date
2026-04-22
🏛️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 contribution of this work in terms of its originality and broader impact is summarized in the results section.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Air Traffic Controllers can issue commands such as altitude changes, heading adjustments, or speed modifications to pilots based on the position and velocity of aircraft in the airspace.
📊
Key Findings & Data:Reducing fuel consumption not only offers economic benefits but also contributes significantly to lowering carbon emissions and promoting environmental sustainability.
💡
Conclusion & Impact:The contribution of this work in terms of its originality and broader impact is summarized in the results section.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:ÇAKIŞMA ÖNLEME · SURDURULEBİLİR HAVACILIK · OPTİMİZASYON

Abstract

Air Traffic Controllers can issue commands such as altitude changes, heading adjustments, or speed modifications to pilots based on the position and velocity of aircraft in the airspace. While these instructions play a crucial role in maintaining airspace safety and have been the focus of numerous studies in the literature, the process of aircraft returning to their original trajectories after conflict-avoidance maneuvers emerges as a distinct and less explored problem. On the other hand, fuel costs constitute one of the largest components of total operational expenses in the aviation industry. Reducing fuel consumption not only offers economic benefits but also contributes significantly to lowering carbon emissions and promoting environmental sustainability. This study proposes an approach to determine the type and timing of maneuvers required for an aircraft to return to its original route after conflict-avoidance commands involving changes in heading, altitude, or speed. The objective is to enhance flight performance while minimizing fuel consumption. Additionally, the fuel cost incurred during the return phase is directly influenced by the initial conflict-avoidance maneuvers. In this study, a mathematical model was developed to optimize return-to-route scenarios following conflict-avoidance actions. A user-friendly decision support system was also designed using the Python programming language to assist air traffic controllers. Simulations conducted using the ESCAPE flight simulator analyzed fuel consumption and carbon emissions by taking variables such as speed, altitude, and heading as inputs and fuel consumption as the dependent variable. Furthermore, this study contributes to the United Nations Sustainable Development Goal of “Affordable and Clean Energy” by offering a potential solution to reduce fuel consumption in high-energy-demand aviation operations. The contribution of this work in terms of its originality and broader impact is summarized in the results section.

Özet

Hava Trafik Kontrolörleri, hava sahasındaki uçakların konum ve hız bilgilerine göre gerekli durumlarda pilotlara irtifa, uçuş açısı, hız değişikliği gibi talimatlar verebilmektedir. Bu talimatlar, hava sahası güvenliğinin sağlanmasında önemli rol oynarken literatürde de birçok çalışmanın odak noktası olmuştur. Ancak çakışma önleyici bu değişikliklerin ardından uçakların kendi yörüngelerine dönüş süreci, ayrı bir problem olarak karşımıza çıkmaktadır. Bu alandaki çalışmalar ise sınırlı sayıda kalmıştır. Öte yandan havayolu taşımacılığı sektöründe yakıt maliyetleri, toplam operasyonel giderlerin en büyük kalemlerinden birini oluşturmaktadır. Bununla birlikte, yakıt tüketiminin azaltılması yalnızca ekonomik fayda sağlamakla kalmayıp aynı zamanda karbon salınımını düşürmek ve çevresel sürdürülebilirlik açısından önemlidir. Bu çalışma, uçuş esnasında çakışmaları önlemek amacıyla verilen yön, irtifa ya da hız değişiklikleri komutları sonrası uçağın kendi orijinal rotasına dönüşü için gerçekleştirmesi gereken manevraların türünü ve zamanlamasını hem uçuş performansını artıracak hem de yakıt tüketimini azaltacak şekilde belirleyecek bir yaklaşım önermektedir. Ayrıca dönüş sürecindeki yakıt maliyeti, önceden verilen çakışma önleyici manevralarla doğrudan ilişkilidir. Bu çalışmada çakışma önleyici faaliyetler sonrası rotaya dönüş senaryolarını optimize edecek bir matematiksel model geliştirilmiş ve Python programlama diliyle, hava trafik kontrolörüne yardımcı olacak kullanıcı dostu bir bilgi sistemi tasarlanmıştır. ESCAPE uçuş simülatöründe yapılan çalışmalarla elde edilen sonuçlar; hız, irtifa, yön komutları gibi değişkenleri girdi, yakıt tüketimini ise bağımlı değişken alarak yakıt tüketimi ve karbon emisyonu açısından analiz edilmiştir. Bu çalışma ayrıca, Birleşmiş Milletler Sürdürülebilir Kalkınma Amaçları’ndan biri olan “Erişilebilir ve Temiz Enerji” hedefi doğrultusunda yüksek enerji tüketimli hava araçlarında yakıt tasarrufu sağlama potansiyeline katkı sunmaktadır. Özgün değer ve yaygın etki açısından çalışmamızın katkısı sonuçlar bölümünde özetlenmiştir.

🔖

Cite This Article

Aktürk, E., Aldanmaz, B., Tuğcu, Ş., Sağır, M. (2026). OPTIMIZING FUEL-EFFICIENT TRAJECTORY RECOVERY AFTER AIRCRAFT CONFLICT-AVOIDANCE MANEUVERS: A DECISION SUPPORT APPROACH. Endüstri Mühendisliği, Cilt: 37 Sayı: 1, 151–178. https://doi.org/10.46465/endustrimuhendisligi.1789091

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (47)

  1. Alonso-Ayuso, A., Escudero, L. F., ve Martín-Campo, F. J. (2016). An exact multi-objective mixed integer nonlinear optimization approach for aircraft conflict resolution. TOP, 24(2), 381–408. Doi: https://doi.org/10.1007/ s11750-015-0373-0
  2. Borjigin, S., ve Austin, M. (2024). Multi-domain semantic model and real-time planning approach to safe and efficient airport taxiway operations. IEEE Transactions on Intelligent Transportation Systems. Doi: https://doi.org/10.1109/TITS.2024.3354242
  3. Bowe, A. R., ve Santiago, C. H. (2012). A fuel-efficient conflict resolution maneuver for air traffic control. NASA. Erişim adresi: https://ntrs.nasa.gov/api/citations/ 20130010956/downloads/20130010956.pdf
  4. Cai, J., ve Zhang, N. (2019). Mixed integer nonlinear programming for aircraft conflict avoidance by applying velocity and altitude changes. Arabian Journal for Science and Engineering, 44(10), 8893–8903. Doi: https://doi.org/10.1007/s13369-019-03911-w
  5. Cai, Y., ve Zhang, Y. (2019). Fuel-efficient conflict resolution with speed and altitude changes. Journal of Aerospace Information Systems, 16(6), 235–248. Doi: https://doi.org/10.2514/1.I010738
  6. Çeçen, R. K. (2021a). A mixed integer linear programming approach for aircraft conflict detection and resolution problem. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 13(2), 350–358. Doi: https://doi.org/10.29137/umagd.736065
  7. Costea, M.-L., Nae, C., Apostolescu, N., Costache, F., Andrei, I.-C., Stroe, G.-L., ve Semenescu, A. (2020). Automatic aircraft collisions algorithm development for civil aircraft. In 2020 10th International Conference on Advanced Computer Information Technologies (ACIT) (pp. 98–103). Doi: https://doi.org/10.1109/ACIT49673.2020.9208865
  8. Deng, W., Zhang, L., Zhou, X., Zhou, Y., Sun, Y., Zhu, W., Chen, H., Deng, W., Chen, H., ve Zhao, H. (2022). Multi-strategy particle swarm and ant colony hybrid optimization for airport taxiway planning problem. Information Sciences. Erişim adresi: https://www.sciencedirect.com/
  9. Dias, F., ve Rey, D. (2024). Aircraft conflict resolution with trajectory recovery using mixed-integer programming. Journal of Global Optimization, 90(4), 1031–1067. https://doi.org/10.1007/s10898-024-01393-1
  10. Dönmez, K., Aydoğan, E., Çetek, C., ve Maraş, E. E. (2023). The impact of taxiway system development stages on runway capacity and delay under demand volatility. Aerospace, 10(1), 6. Doi: https://doi.org/10.3390/ aerospace10010006
  11. Dudoit, A., Rimša, V., Bogdevičius, M., ve Skorupski, J. (2022). Effectiveness of conflict resolution methods in air traffic management. Aerospace, 9(2), 112. Doi: https://doi.org/10.3390/aerospace9020112
  12. Federal Aviation Administration. (2021). Aeronautical Information Manual. Erişim adresi: https://www.faa.gov/air_traffic/ publications/atpubs/aim_html/
  13. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.
  14. Haupt, R. L. ve Haupt, S. E. (2004). Practical genetic algorithms. Hoboken, NJ: John Wiley & Sons.
  15. Hwang, I., Kim, J., ve Tomlin, C. (2007). Protocol-based conflict resolution for air traffic control. Air Traffic Control Quarterly, 15(1), 1–34. Doi: https://doi.org/10.2514/ATCQ.15.1.1
  16. International Civil Aviation Organization. (2022). Procedures for Air Navigation Services: Air Traffic Management (Doc 4444). International Civil Aviation Organization. Erişim adresi: https://www.icao.int/
  17. Jamal, M., Ashraf, H., ve Jhanjhi, N. Z. (2023). Machine learning-based aircraft conflict prediction: A systematic literature review. Preprints. Doi: https://doi.org/10.20944/preprints202312.1600.v1
  18. Jiang, X., ve Hao, P. (2024). Hub airport end-around taxiway construction planning development: A review. Applied Sciences, 14(8), 3500. Doi: https://doi.org/10.3390/app14083500
  19. Khadilkar, H., ve Balakrishnan, H. (2011a). Estimating the impact of air traffic flow management initiatives on fuel consumption and emissions. In AIAA Aviation Technology, Integration, and Operations Conference. Erişim adresi: https://arc.aiaa.org/
  20. Kopardekar, P., Bilimoria, K., ve Sridhar, B. (2009b). Initial concepts for airspace configuration and traffic flow management under NextGen. In AIAA Aviation Technology, Integration, and Operations Conference. Erişim adresi: https://arc.aiaa.org/
  21. Lehouillier, T., Nasri, M. I., Soumis, F., Desaulniers, G., ve Omer, J. (2017). Solving the air conflict resolution problem under uncertainty using an iterative biobjective mixed integer programming approach. Transportation Science, 51(4), 1242–1258. Doi: https://doi.org/10.1287/trsc.2016.0714
  22. Li, J., Zhang, H., Gong, M., Liang, Z., Liu, W., Tong, Z., Yi, L., Morris, R., Pasareanu, C., ve Koenig, S. (2019). Scheduling and airport taxiway path planning under uncertainty. In AIAA Aviation 2019 Forum, 2930. Doi: https://doi.org/10.2514/6.2019-2930
  23. Liu, X., ve Xiao, G. (2023). Multi-aircraft flight conflict resolution and trajectory recovery scheme based on mixed integer linear programming and geometric rules. Transportation Research Record, 2677(2), 411–423. Doi: https://doi.org/10.1177/03611981221128811
  24. Omer, J. (2015a). A space-discretized mixed-integer linear model for air-conflict resolution with speed and heading maneuvers. Computers & Operations Research, 58, 75–86. Doi: https://doi.org/10.1016/j.cor.2014.12.012
  25. Omer, J. (2015b). Comparison of mixed-integer linear models for fuel-optimal air conflict resolution with recovery. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3126–3137. Doi: https://doi.org/10.1109/TITS.2015.2434942
  26. Özdemir, M. (2024). Hava trafik kontrolde ayırma yöntemlerinin uçak emisyonları üzerindeki etkisinin optimizasyonu (Yüksek lisans tezi). Eskişehir Teknik Üniversitesi.
  27. Paielli, R. A., ve Erzberger, H. (1997). Conflict probability estimation for free flight. Journal of Guidance, Control, and Dynamics, 20(3), 588–596. Doi: https://doi.org/10.2514/2.4081
  28. Pant, K. A., Lin, L.-Y., Sribunma, W., Brunswicker, S., Goppert, J. M., ve Hwang, I. (2025). SAFE-TAXI: A hierarchical multi-UAS safe auto-taxiing framework with runtime safety assurance and conflict resolution. arXiv. Erişim adresi: https://arxiv.org/abs/2501.12779
  29. Reynolds, T. (2014). Fuel efficiency in modern air transport. Journal of Air Transport Management, 37, 1–9. Doi: https://doi.org/10.1016/j.jairtraman.2014.02.002
  30. SKYbrary. (2022). Aircraft conflict types. Erişim adresi: https://skybrary.aero/articles/aircraft-conflict-types
  31. Srivastava, A., Tomlin, C., ve Erzberger, H. (2022). Efficient heading change strategies for aircraft conflict resolution. Journal of Guidance, Control, and Dynamics, 45(3), 456–468. Doi: https://doi.org/10.2514/1.G006541
  32. Sun, F., Chen, Y., Xu, X., Mu, Y., ve Wang, Z. (2022). Velocity obstacle-based conflict resolution and recovery method. The Aeronautical Journal, 126(1295), 345–364. Doi: https://doi.org/10.1017/aer.2021.67
  33. Tian, J., Liu, J., Ma, W. ve Poznyak, A. (2018). Multi-objective genetic algorithm for aircraft conflict resolution problem. International Journal of Systems Science, 49(14), 2883–2899. Doi: https://doi.org/10.1080/00207721.2018.1524108
  34. Zhang, J., ve Yu, P. (2024). Research on airport runway-taxiway system controlling optimization based on scheduling algorithm. In Proceedings of SPIE 13251, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 1325135. Doi: https://doi.org/10.1117/12.3039849
  35. Zhang, M., Yu, J., Zhang, Y., Wang, S., ve Yu, H. (2017). Flight conflict resolution during low-altitude rescue operation based on ensemble conflict models. Advances in Mechanical Engineering, 9(4). Doi: https://doi.org/10.1177/1687814017696655
  36. Zhang, T., Ding, M., Wang, B., ve Chen, Q. (2016). Conflict-free time-based trajectory planning for aircraft taxi automation with refined taxiway modeling. Journal of Advanced Transportation, 50(7), 1344–1360. Doi: https://doi.org/10.1002/atr.1324
  37. Zhao, N., Li, N., Sun, Y., ve Zhang, L. (2021). Research on aircraft surface taxi path planning and conflict detection and resolution. Journal of Advanced Transportation, 2021, 9951206. https://doi.org/10.1155/2021/9951206
  38. Zhao, P., ve Liu, Y. (2020). Efficient multiple aircraft conflict resolution using A algorithm and indexing method. In AIAA Aviation 2020 Forum*. Doi: https://doi.org/10.2514/6.2020-2918
  39. Zhu, X., Xu, C., Qu, J., ve Su, T. (2022). Design and simulation-based evaluation of taxiway operation scheme for multi-runway airport maneuvering area. Journal of System Simulation, 34(11), 2448–2457. Doi: https://doi.org/10.16182/j.issn1004731x.joss.21-0789
  40. Bowe, A. R., ve Santiago, C. H. (2012). A fuel-efficient conflict resolution maneuver for air traffic control. NASA. Erişim adresi: https://ntrs.nasa.gov/api/citations/ 20130010956/downloads/20130010956.pdf
  41. Deng, W., Zhang, L., Zhou, X., Zhou, Y., Sun, Y., Zhu, W., Chen, H., Deng, W., Chen, H., ve Zhao, H. (2022). Multi-strategy particle swarm and ant colony hybrid optimization for airport taxiway planning problem. Information Sciences. Erişim adresi: https://www.sciencedirect.com/
  42. Federal Aviation Administration. (2021). Aeronautical Information Manual. Erişim adresi: https://www.faa.gov/air_traffic/ publications/atpubs/aim_html/
  43. International Civil Aviation Organization. (2022). Procedures for Air Navigation Services: Air Traffic Management (Doc 4444). International Civil Aviation Organization. Erişim adresi: https://www.icao.int/
  44. Khadilkar, H., ve Balakrishnan, H. (2011a). Estimating the impact of air traffic flow management initiatives on fuel consumption and emissions. In AIAA Aviation Technology, Integration, and Operations Conference. Erişim adresi: https://arc.aiaa.org/
  45. Kopardekar, P., Bilimoria, K., ve Sridhar, B. (2009b). Initial concepts for airspace configuration and traffic flow management under NextGen. In AIAA Aviation Technology, Integration, and Operations Conference. Erişim adresi: https://arc.aiaa.org/
  46. Pant, K. A., Lin, L.-Y., Sribunma, W., Brunswicker, S., Goppert, J. M., ve Hwang, I. (2025). SAFE-TAXI: A hierarchical multi-UAS safe auto-taxiing framework with runtime safety assurance and conflict resolution. arXiv. Erişim adresi: https://arxiv.org/abs/2501.12779
  47. SKYbrary. (2022). Aircraft conflict types. Erişim adresi: https://skybrary.aero/articles/aircraft-conflict-types