Supply-Demand Matching Estimation with Machine Learning in Airline Planning Process
DOI:
https://doi.org/10.5281/zenodo.21002611Parole chiave:
Aircraft Tail Assignment, Machine Learning, Supply-Demand Matching, Random Forest, Airline Planning, SustainabilityAbstract
Airline companies operate in a highly competitive and uncertain environment where efficient resource allocation is essential for maximizing economic performance and supporting sustainability objectives. One of the major challenges in airline planning is matching aircraft capacity with uncertain passenger demand while maintaining operational efficiency. This study proposes a machine learning-based decision support framework that integrates demand uncertainty with aircraft tail assignment. Three nonlinear regression models—Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel, Polynomial Regression, and Random Forest Regression—were employed to predict flight-based economic returns using real-world airline operational data, including aircraft type, passenger demand, and cargo volume. In addition, a bounded prediction mechanism incorporating aircraft capacity constraints was introduced to ensure operational feasibility. Model performance was evaluated using R², Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and cross-validation. At the end of the study, all three regression models achieved a high accuracy rate in representing the data, but the SVR-RBF model performed best. Furthermore, the proposed framework allowed for the determination of demand thresholds for optimal aircraft type selection, contributing to increased economic efficiency and reduced unnecessary capacity utilization. The findings demonstrate that machine learning-based predictive models can effectively support airline planning processes and indirectly contribute to sustainability goals.
Riferimenti bibliografici
Abara, J. (1989). Applying integer linear programming to the fleet assignment problem. Interfaces, 19(4), 20-28.
Akıl, S. et al. (2024). Exploring machine learning techniques for oil price forecasting: A comparative study of SVM, SMO, and SGD-base models, Procedia Computer Science 232, P.924–933, https://doi.org/10.1016/j.procs.2024.01.092.
Arslankaya, S., & Toprak, Ş. (2021). Makine Öğrenmesi ve Derin Öğrenme Algoritmalarını Kullanarak Hisse Senedi Fiyat Tahmini. International Journal of Engineering Research and Development, 13(1), 178-192. https://doi.org/10.29137/umagd.771671
Barnhart, C. et al., (2002). Itinerary-based airline fleet assignment. Transportation science, 36(2), 199-217.
Dezsö V. et al., (2023). Prediction of severity of aviation landing accidents using support vector machine models, Accident Analysis & Prevention, 187, 107043, https://doi.org/10.1016/j.aap.2023.107043
Gao, J., (2023). R-Squared (R2) – How much variation is explained?, Research Methods in Medicine & Health Sciences. 2023;5(4):104-109. https://doi.org/10.1177/26320843231186398.
Geursen, I. L., Santos, B. F., & Yorke-Smith, N. (2023). Fleet planning under demand and fuel price uncertainty using actor–critic reinforcement learning. Journal of Air Transport Management, 110, 102401. https://doi.org/10.1016/j.jairtraman.2023.102401
Ghattas, B., & Manzon, D. (2023). Machine learning alternatives to response surface models. Mathematics, 11(15), 3406. https://doi.org/10.3390/math11153406
Glomb L. et al., (2024). Fleet & tail assignment under uncertainty, Discrete Optimization, 52, 100836, https://doi.org/10.1016/j.disopt.2024.100836.
Justin C. et al., (2022). Integrated fleet assignment and scheduling for environmentally friendly electrified regional air mobility, Transportation Research Part C: Emerging Technologies, 138, 103567, https://doi.org/10.1016/j.trc.2022.103567.
Karanki, F., and Yu, Chunyan, (2026). The asymmetric impact of sustainable aviation fuel adoption on airline industry, Transport Policy, 180, 104059, https://doi.org/10.1016/j.tranpol.2026.104059
Kenan, N., Jebali, A., & Diabat, A. (2017). An integrated flight scheduling and fleet assignment problem under uncertainty. Comput. Oper. Res., 100, 333-342. https://doi.org/10.1016/j.cor.2017.08.014.
Kiracı, K. Akan, N., Akan, E., (2026). Exploring the impact of sustainability components on financial performance: Evidence from the airline industry, Journal of the Air Transport Research Society, 6, https://doi.org/10.1016/j.jatrs.2026.100105
Kleywegt, A. J., Shapiro, A., & Homem-De-Mello, T. (2002). The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization, 12(2), 479-502. https://doi.org/10.1137/S1052623499363220
Liu, M., et al., (2023). Green Airline-Fleet Assignment with Uncertain Passenger Demand and Fuel Price. Sustainability, 15(2), 899. https://doi.org/10.3390/su15020899
Lohatepanont, M., & Barnhart, C. (2004). Airline schedule planning: Integrated models and algorithms for schedule design and fleet assignment. Transportation science, 38(1), 19-32.
Maduranga, M. W. P. (2024). Improved-RSSI-based indoor localization for Internet of Things (IoT) applications using machine learning regressors. Journal of Engineering and Applied Science, 71(1),38. https://doi.org/10.48550/arXiv.2402.11433
Martín-Domingo, L., Efthymiou, M., Mota, M. M., (2025). Airline sustainability reporting in Europe: Progress, compliance and challenges, Environmental and Sustainability Indicators, 28, 101008, https://doi.org/10.1016/j.indic.2025.101008.
Moreno R. J. et al., (2023). Tail Assignment-Driven Aircraft Routing Model, Transportation Research Procedia, 71, P. 172-179, https://doi.org/10.1016/j.trpro.2023.11.072.
Omrani, F. et al., (2024). Assessment of aviation accident datasets in severity prediction through machine learning, Journal of Air Transport Management, 115, 102531, https://doi.org/10.1016/j.jairtraman.2023.102531.
Paul, s., Witter, j., & Chowdhury, s. (2024). Graph learning-based fleet scheduling under demand uncertainty. arxiv preprint arxiv:2401.04851. https://arxiv.org/abs/2401.04851
Rajawat, A., S., (2022). Chapter six- Renewable energy system for industrial internet of things model using fusion-AI, Editor(s): Rabindra Nath Shaw, Ankush Ghosh, Saad Mekhilef, Valentina Emilia Balas, Applications of AI and IOT in Renewable Energy, Academic Press, P. 107-128, https://doi.org/10.1016/B978-0-323-91699-8.00006-1.
Setiadi, D. R. I. M. et al., (2023). Digital image steganography survey and investigation (goal, assessment, method, development, and dataset), Signal Processing, 206, 108908, https://doi.org/10.1016/j.sigpro.2022.108908.
Sherali, H., & Zhu, X. (2008). Two-Stage Fleet Assignment Model Considering Stochastic Passenger Demands. Oper. Res., 56, 383-399. https://doi.org/10.1287/opre.1070.0476.
Singh, S. K. et al., (2023). A state-of-the-art review on the utilization of machine learning in nanofluids, solar energy generation, and the prognosis of solar power, Engineering Analysis with Boundary Elements, 155, P. 62-86, https://doi.org/10.1016/j.enganabound.2023.06.003.
Smola, Alex & Schölkopf, Bernhard. (2004). A tutorial on support vector regression. Statistics and Computing. 14. 199-222. 10.1023/B%3ASTCO.0000035301.49549.88.
Vinod, P.V. et al. (2024). A novel multitask transformer deep learning architecture for joint classification and segmentation of horticulture plantations using very High-Resolution satellite imagery, Computers and Electronics in Agriculture, 227, Part 1, 109540, https://doi.org/10.1016/j.compag.2024.109540.
Vos, K.Peng, et al., (2023). Aircraft fleet availability optimisation: a reinforcement learning approach,The Aeronautical Journal 127(1318), https://doi.org/10.1017/aer.2023.104
Wu, M. et al. (2025). Predictive modeling of eye lens dose in interventional radiology: A polynomial regression approach to cumulative fluoroscopy dose. Frontiers in Public Health, 13, 1547101. DOI: 10.3389/fpubh.2025.1547101
Yazıcı, İ. et al., (2023). A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems, Engineering Science and Technology, an International Journal, 44, 101455, https://doi.org/10.1016/j.jestch.2023.101455
Yildiz, B., Acikgoz, E., Oner, G., (2026). Financial sustainability in the airline industry: Panel evidence from leading global carriers, Transport Policy, 181,104085, https://doi.org/10.1016/j.tranpol.2026.104085.
Zheng, Y. et al. (2024). Modeling and detection of low-altitude flight conflict network based on SVM, Measurement: Sensors, 31, 100954, https://doi.org/10.1016/j.measen.2023.100954
##submission.downloads##
Pubblicato
Come citare
Fascicolo
Sezione
Licenza
Copyright (c) 2026 International Journal of Contemporary Economics and Administrative Sciences
TQuesto lavoro è fornito con la licenza Creative Commons Attribuzione 4.0 Internazionale.
The Author(s) must make formal transfer of copyright for each article prior to publication in the International Journal of Contemporary Economics and Administrative Sciences. Such transfer enables the Journal to defend itself against plagiarism and other forms of copyright infringement. Your cooperation is appreciated. You agree that copyright of your article to be published in the International Journal of Contemporary Economics and Administrative Sciences is hereby transferred, throughout the World and for the full term and all extensions and renewals thereof, to International Journal of Contemporary Economics and Administrative Sciences.
The Author(s) reserve(s): (a) the trademark rights and patent rights, if any, and (b) the right to use all or part of the information contained in this article in future, non-commercial works of the Author's own, or, if the article is a "work-for-hire" and made within the scope of the Author's employment, the employer may use all or part of the information contained in this article for intra-company use, provided the usual acknowledgements are given regarding copyright notice and reference to the original publication.
The Author(s) warrant(s) that the article is Author's original work, and has not been published before. If excerpts from copyrighted works are included, the Author will obtain written permission from the copyright owners and shall credit the sources in the article. The author also warrants that the article contains no libelous or unlawful statements, and does not infringe on the rights of others. If the article was prepared jointly with other Author(s), the Author agrees to inform the co-Author(s) of the terms of the copyright transfer and to sign on their behalf; or in the case of a "work-for-hire" the employer or an authorized representative of the employer.
The journal is registered with the ISSN : 1925-4423.
IJCEAS is licensed under a Creative Commons Attribution 4.0 International License.
This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.
