Modeling for Fuel Policy in Divert Risk Due to Low Visibility Operations in Airline Operations

Abstract

Fuel costs have the highest share in airline operating costs. Therefore, fuel efficiency is important for cost optimization in airline operation management. The cost optimization due to additional fuel transportation caused by meteorological conditions is also an important field. This study aims to provide data for making a decision to form a fuel policy for a flight schedule by determining the divert risk ratio by analyzing previous meteorology reports when there is a risk of diverting due to low visibility in meteorology reports. Thus, by providing data for airline operation managers to make decisions that will provide fuel optimization, cost-effectiveness will also be contributed. Data for the study were obtained from OGIMET (the Spanish meteorology website). A simulated flight was created, and the past period forecast efficiency was calculated from previous meteorological data for risk ratio. Then, it was revealed how much the divert risk ratio would reduce if additional fuel was carried for the flight according to past period meteorological data to ensure the aircraft for waiting in the air. As a result, a model has been created relating to how to determine the divert risk ratio to make a fuel policy decision for a flight schedule.

Keywords

• Airline operation management
• low visibility
• fuel policy
• airline fuel efficiency
• divert risk ratio

References

1. Abdelghany, K. F., Abdelghany, A. F., & Ekollu, G. (2008). An integrated decision support tool for airlines schedule recovery during irregular operations. European Journal of Operational Research, 185(2), 825–848. https://doi.org/10.1016/j.ejor.2006.12.045google scholar
2. Abdelghany, K., Abdelghany, A., & Raina, S. (2005). A model for the airlines’ fuel management strategies. Journal of Air Transport Management, 11(4), 199–206. https://doi.org/10.1016/j.jairtraman.2004.10.002 google scholar
3. Ateş, S. S., Kafalı, H., & Kılıçoğlu, G. C. (2018). Reducing Operational Fuel Costs of Airlines: A Model for Monitoring and Managing Fuel Consumption Using Unified Modeling Language. Celal Bayar University Journal of Science, 14(1), 105-111. https://doi.org/10.18466/cbayarfbe.372436 google scholar
4. Ba, Z., Liu, L., & Xia, Y. (2024). Multidimensional policy citation features: Insights into policymakers’ policy adoption decision-making. Government Information Quarterly, 42(1), 102004–102004. https://doi.org/10.1016/j.giq.2024.102004 google scholar
5. Beltman, M., Ribeiro, M., de Wilde, J., & Sun, J. (2025). Dynamically forecasting airline departure delay probability distributions for individual flights using supervised learning. Journal of Air Transport Management, 126, 102788. https://doi.org/10.1016/j.jairtraman.2025.102788 google scholar
6. Brueckner, J. K., & Abreu, C. (2020). Does the fuel-conservation effect of higher fuel prices appear at both the aircraft-model and aggregate airline levels? Economics Letters, 197, 109647. https://doi.org/10.1016/j.econlet.2020.109647 google scholar
7. Csereklyei, Z., & Stern, D. I. (2020). Flying More Efficiently: Joint Impacts of Fuel Prices, Capital Costs and Fleet Size on Airline Fleet Fuel Economy. Ecological Economics, 175, 106714. https://doi.org/10.1016/j.ecolecon.2020.106714 google scholar
8. Guo, L., Tao, J., Wang, C., Zhang, M., & Sun, G. (2020). Fuel efficiency optimization of high-aspect-ratio aircraft via variable camber technology considering aeroelasticity. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 095441002095996. https://doi.org/10.1177/0954410020959964 google scholar

9. Harris, G. R., & Harris, G. R. (2026). Comparison of different scoring methods for TAFs and other probabilistic forecasts. Confex.com; AMS. https://ams.confex.com/ams/May2000/webprogram/Paper13166.html google scholar
10. Huang, C., & Cheng, X. (2022). Estimation of aircraft fuel consumption by modeling flight data from avionics systems. Journal of Air Transport Management, 99, 102181. https://doi.org/10.1016/j.jairtraman.2022.102181 google scholar
11. ICAO. (2019). ICAO; icao. https://store.icao.int/en/annex-10-aeronautical-telecommunications-volume-i-radio-navigational-aids google scholar
12. ICAO. (2019). ICAO; icao. https://store.icao.int/en/annex-3-meteorological-service-for-international-air-navigation-1 google scholar
13. ICAO. (2019). ICAO; icao. https://store.icao.int/en/location-indicators-doc-7910google scholar
14. Inan, I., Orhan, I., & Ekici, S. (2025). Fuel Savings Strategies for Sustainable Aviation in Accordance with United Nations Sustainable Development Goals (UN SDGs). Energy, 135159. https://doi.org/10.1016/j.energy.2025.135159 google scholar
15. Jiang, C., & Liu, Y. (2025). The role of sustainable aviation fuel in CORSIA: An economic analysis. Energy Economics, 143, 108238. https://doi.org/10.1016/j.eneco.2025.108238 google scholar
16. Kang, L., & Hansen, M. (2018). Improving airline fuel efficiency via fuel burn prediction and uncertainty estimation. Transportation Research Part C: Emerging Technologies, 97, 128–146. https://doi.org/10.1016/j.trc.2018.10.002 google scholar
17. Kheraie, A. Z., & Mahmassani, H. S. (2012). Leveraging Fuel Cost Differences in Aircraft Routing by considering Fuel Ferrying Strategies. Transportation Research Record Journal of the Transportation Research Board, 2300(1), 139–146. https://doi.org/10.3141/2300-16 google scholar
18. Kwan, I., & Rutherford, D. (2015). Assessment of U.S. Domestic Airline Fuel Efficiency since 2010. Transportation Research Record: Journal of the Transportation Research Board, 2501(1), 1–8. https://doi.org/10.3141/2501-01 google scholar
19. Malandri, C., Mantecchini, L., Paganelli, F., & Nadia Postorino, M. (2020). Impacts of unplanned aircraft diversions on airport ground operations. Transportation Research Procedia, 47, 537–544. https://doi.org/10.1016/j.trpro.2020.03.129 google scholar
20. METEOSAT / metar reports information. (2025). Ogimet.com. https://www.ogimet.com/cgi-bin/gmsat2?lang=en google scholar
21. Ogunsina, K., Bilionis, I., & DeLaurentis, D. (2021). Exploratory data analysis for airline disruption management. Machine Learning with Applications, 100102. https://doi.org/10.1016/j.mlwa.2021.100102google scholar
22. Patriarca, R., Simone, F., & Di Gravio, G. (2023). Supporting weather forecasting performance management at aerodromes through anomaly detection and hierarchical clustering. Expert Systems with Applications, 213, 119210. https://doi.org/10.1016/j.eswa.2022.119210 google scholar
23. Pei, S., He, Y., Fan, Z., & Zhang, B. (2020). Decision support system for the irregular flight recovery problem. Research in Transportation Business & Management, 100501. https://doi.org/10.1016/j.rtbm.2020.100501 google scholar
24. Precision Approach. (2020, December 29). SKYbrary Aviation Safety. https://skybrary.aero/articles/precision-approach google scholar
25. Raeder, F. T. (2025). Airfares adjustment to fuel price shocks: Evidence from the Brazilian airline market. Journal of Air Transport Management, 125, 102785. https://doi.org/10.1016/j.jairtraman.2025.102785 google scholar
26. Reasons an Aircraft Dispatcher May Divert a Flight. (n.d.). Sheffield School of Aeronautics. https://www.sheffield.com/articles/reasons-an-aircraft-dispatcher-may-divert-a-flight google scholar
27. Süzer, A. S. (2023). Dispeç Kaynaklı Aksaklıkların Diğer Operasyon Birimlerine Etkisi. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 6(1), 123–138. https://doi.org/10.51513/jitsa.1239693 google scholar
28. Swidan, H., Merkert, R., & Kwon, O. K. (2019). Designing optimal jet fuel hedging strategies for airlines – Why hedging will not always reduce risk exposure. Transportation Research Part A: Policy and Practice, 130, 20–36. https://doi.org/10.1016/j.tra.2019.09.014google scholar
29. Şafak, Ö., Çavuş, Ö., & AktürkM. S. (2022). A two-stage decision dependent stochastic approach for airline flight network expansion. Transportation Research Part B: Methodological, 158, 78–101. https://doi.org/10.1016/j.trb.2022.02.003 google scholar
30. Ullah, A., Biao, H., Sarwar, S., & Wu, Z. (2025). Transmission of oil price risk to airline stock returns: Evidence from China and the United States. Research in Transportation Economics, 110, 101532. https://doi.org/10.1016/j.retrec.2025.101532 google scholar
31. What Happens When An Aircraft Diverts? (2021, June 26). Simple Flying. https://simpleflying.com/what-happens-when-an-aircraft-diverts/ google scholar
32. Wu, Y., Mei, G., & Shao, K. (2022). Revealing influence of meteorological conditions and flight factors on delays Using XGBoost. Journal of Computational Mathematics and Data Science, 3, 100030. https://doi.org/10.1016/j.jcmds.2022.100030 google scholar
33. Yung, N., Wu, C.-L., & Tan, D. (2020). Evaluating the implementation of performance-based fuel uplift regulation for airline operation. Transportation Research. Part A, Policy and Practice, 133, 47–61. https://doi.org/10.1016/j.tra.2019.12.028 google scholar
34. Zheng, S., Wang, C., & Jiang, C. (2024). Carrot or stick? Environmental and welfare implications of sustainable aviation fuel policies. Transportation Research Part B: Methodological, 188, 103062. https://doi.org/10.1016/j.trb.2024.103062 google scholar