LEVERAGING A CENTRALIZED FLEET ASSIGNMENT MANAGEMENT FOR AN AVIATION RESILIENCE

Abstract

Since deregulation in the aviation industry, competition among airlines has intensified. This competition is shown by the increasing number of routes, service times, aircraft, and airports served. This competition causes not all flight services to meet their targets, resulting in aircraft operating less efficiently. Passenger seats are not filled, and flight delays are becoming more frequent. Obviously, this will negatively impact consumers and the airline's finances. The study focused on assigning the fleet used to serve flights to maintain existing aviation services and improve aircraft operational efficiency. This study aims to maximize profits by optimizing fleet assignments through the Centralized Fleet Assignment Management (CFAM) strategy. In general, airlines regulate the assignment of their respective fleets. However, in this CFAM, the fleet assignment strategy is carried out centrally by a single management, as if it were a joint company. Integer Programming was used in this study to optimize the aviation fleet assignment model. The purpose of this model is to determine the most appropriate fleet for flights to maximize profits. The results of the study show that the CFAM strategy generates higher profits than in previous conditions, and the aircraft assignment composition is also obtained. With this strategy, evidently, airlines as a system will benefit financially, helping them avoid potential bankruptcy that could have serious consequences for flights. The strategy also offers a different perspective and new nuances to flight service management. In addition, the most important result of this study is that it can serve as a basis for government policy, in this case as a regulator of air transportation services, to ensure the continued operation of aviation services.

Bahasa Abstract

Sejak deregulasi di industri penerbangan, persaingan antar maskapai penerbangan semakin intensif. Persaingan ini ditunjukkan dengan meningkatnya jumlah rute, waktu layanan, pesawat, dan bandara yang dilayan. Persaingan ini menyebabkan tidak semua layanan penerbangan memenuhi targetnya, sehingga pesawat beroperasi kurang efisien. Kursi penumpang tidak terisi, dan penundaan penerbangan menjadi lebih sering. Jelas, ini akan berdampak negatif pada konsumen dan keuangan maskapai. Studi ini berfokus pada penugasan armada yang digunakan untuk melayani penerbangan untuk mempertahankan layanan penerbangan yang ada dan meningkatkan efisiensi operasional pesawat. Penelitian ini bertujuan untuk memaksimalkan keuntungan dengan mengoptimalkan penugasan armada melalui strategi Centralized Fleet Assignment Management (CFAM). Secara umum, maskapai penerbangan mengatur penugasan armada masing-masing. Namun, dalam CFAM ini, strategi penugasan armada dilakukan secara terpusat oleh satu manajemen, seolah-olah itu adalah perusahaan gabungan. Integer Programming digunakan dalam penelitian ini untuk mengoptimalkan model penugasan armada. Tujuan dari model ini adalah untuk menentukan armada yang paling tepat untuk penerbangan untuk memaksimalkan keuntungan. Hasil penelitian menunjukkan bahwa strategi CFAM menghasilkan keuntungan yang lebih tinggi daripada kondisi sebelumnya, dan komposisi penugasan pesawat juga diperoleh. Dengan strategi ini, terbukti, gabungan maskapai penerbangan sebagai sistem akan diuntungkan secara finansial, membantu mereka menghindari potensi kebangkrutan yang dapat berdampak serius bagi penerbangan. Strategi ini juga menawarkan perspektif yang berbeda dan nuansa baru untuk manajemen layanan penerbangan. Selain itu, hasil terpenting dari penelitian ini adalah dapat menjadi dasar kebijakan pemerintah, dalam hal ini sebagai regulator layanan transportasi udara, untuk memastikan kelanjutan pengoperasian layanan penerbangan.

References

Abouzeid, A. A., Eldin, M. M., & Razek, M. A. (2021). Particle swarm optimization for airlines fleet assignment. Indonesian Journal of Electrical Engineering and Computer Science, 22(1), 427–434. https://doi.org/10.11591/ijeecs.v22.i1.pp427-434

Adler, N., & Hanany, E. (2016). Regulating inter-firm agreements: The case of airline codesharing in parallel networks. Transportation Research Part B: Methodological, 84, 31–54. https://doi.org/10.1016/j.trb.2015.12.002

Ahuja, R. K., Goodstein, J., Mukherjee, A., Orlin, J. B., & Sharma, D. (2007). A very large-scale neighborhood search algorithm for the combined through-fleet-assignment model. INFORMS Journal on Computing, 19(3), 416–428. https://doi.org/10.1287/ijoc.1060.0193

Anzoom, R., & Hasin, M. A. A. (2018). Optimal Fleet Assignment Using Ant Colony Algorithm. 2018 International Conference on Production and Operations Management Society, POMS 2018. https://doi.org/10.1109/POMS.2018.8629468

Barnhart, C., Belobaba, P., & Odoni, A. R. (2003). Applications of operations research in the air transport industry. Transportation Science, 37(4), 368–391. https://doi.org/10.1287/trsc.37.4.368.23276

Barnhart, C., Farahat, A., & Lohatepanont, M. (2009). Airline fleet assignment with enhanced revenue modeling. Operations Research, 57(1), 231–244. https://doi.org/10.1287/opre.1070.0503

Bélanger, N., Desaulniers, G., Soumis, F., & Desrosiers, J. (2006). Periodic airline fleet assignment with time windows, spacing constraints, and time dependent revenues. European Journal of Operational Research, 175(3), 1754–1766. https://doi.org/10.1016/j.ejor.2004.04.051

Brander, J. A., & Zhang, A. (1993). Dynamic oligopoly behaviour in the airline industry. International Journal of Industrial Organization, 11(3), 407–435. https://doi.org/10.1016/0167-7187(93)90017-7

Calvet, L. (2024). Towards Environmentally Sustainable Aviation: A Review on Operational Optimization. In Future Transportation (Vol. 4, Issue 2, pp. 518–547). https://doi.org/10.3390/futuretransp4020025

Chen, L., & Han, S. (2025). Sustainable airline operations: A season-based optimization framework for flight scheduling and aircraft assignment. Energy, 340, 139187. https://doi.org/10.1016/j.energy.2025.139187

Chen, Y., Wu, W., & Chen, J. (2023). Research on Fleet Assignment of Airlines in the Context of Carbon Trading. In C. Y., M. J., Z. G., W. H., S. L., & H. Z. (Eds.), CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation - Proceedings of the 23rd COTA International Conference of Transportation Professionals (pp. 60–69). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784484869.007

Ciftci, M. E., & Özkır, V. (2024). Integrated optimisation model for airline bank structure and fleet assignment problem. Annals of Operations Research, 342(1), 265–285. https://doi.org/10.1007/s10479-023-05615-9

Haouari, M., Aissaoui, N., & Mansour, F. Z. (2009). Network flow-based approaches for integrated aircraft fleeting and routing. European Journal of Operational Research, 193(2), 591–599. https://doi.org/10.1016/j.ejor.2007.11.042

Hu, X., Caldentey, R., & Vulcano, G. (2013). Revenue sharing in airline alliances. Management Science, 59(5), 1177–1195. https://doi.org/10.1287/mnsc.1120.1591

Justin, C. Y., Payan, A. P., & Mavris, D. N. (2022). Integrated fleet assignment and scheduling for environmentally friendly electrified regional air mobility. Transportation Research Part C: Emerging Technologies, 138. https://doi.org/10.1016/j.trc.2022.103567

Kemenhub. (2024). Statistik Transportasi Udara Nasional. https://www.dephub.go.id

Kenan, N., Diabat, A., & Jebali, A. (2018). Codeshare agreements in the integrated aircraft routing problem. Transportation Research Part B: Methodological, 117, 272–295. https://doi.org/10.1016/j.trb.2018.08.008

Kenan, N., Jebali, A., & Diabat, A. (2018). An integrated flight scheduling and fleet assignment problem under uncertainty. Computers and Operations Research, 100, 333–342. https://doi.org/10.1016/j.cor.2017.08.014

Klophaus, R., & Lordan, O. (2018). Codesharing network vulnerability of global airline alliances. Transportation Research Part A: Policy and Practice, 111, 1–10. https://doi.org/10.1016/j.tra.2018.02.010

Nahry, S. S. (2000). Optimal scheduling of public transport fleet at network level. Journal of Advanced Transportation, 34(2), 297–323.

Özener, O. Ö., Örmeci Matoğlu, M., Erdoğan, G., Haouari, M., & Sözer, H. (2017). Solving a large-scale integrated fleet assignment and crew pairing problem. Annals of Operations Research, 253(1), 477–500. https://doi.org/10.1007/s10479-016-2319-9

Pramono, A., Middleton, J. H., & Caponecchia, C. (2020). Civil Aviation Occurrences in Indonesia. Journal of Advanced Transportation, 2020(February 2019). https://doi.org/10.1155/2020/3240764

Seyedsayamdost, E. (2020). Sustainable development goals. Essential Concepts of Global Environmental Governance. https://doi.org/10.4324/9780367816681-102

Sherali, H. D., Bae, K. H., & Haouari, M. (2013). A benders decomposition approach for an integrated airline schedule design and fleet assignment problem with flight retiming, schedule balance, and demand recapture. Annals of Operations Research, 210(1), 213–244. https://doi.org/10.1007/s10479-011-0906-3

Sriram, C., & Haghani, A. (2003). An optimization model for aircraft maintenance scheduling and re-assignment. Transportation Research Part A: Policy and Practice, 37(1), 29–48. https://doi.org/10.1016/S0965-8564(02)00004-6

Statista. (2022). Aviation industry in Indonesia - Statistic & Facts. Statista Research Department. https://www.statista.com/topics/5822/aviation-industry-in-indonesia/#topicOverview

Tofigh, A. A., Bashiri, M., Manteghi, M., & Jalil, M. (2015). A new model for fleet assignment problem, case study of Iran Air Network at vision 2036. International Journal of Engineering, Transactions B: Applications, 28(11), 1614–1623. https://doi.org/10.5829/idosi.ije.2015.28.11b.09

Unal, Y. Z., Sevkli, M., Uysal, O., & Turkyilmaz, A. (2022). A new approach to fleet assignment and aircraft routing problems. In T. A. & N. D. (Eds.), Transportation Research Procedia (Vol. 59, pp. 67–75). Elsevier B.V. https://doi.org/10.1016/j.trpro.2021.11.098