Flight Connection Planning for Low-Cost Carriers Under Passenger Demand Uncertainty
As low-cost carriers (LCCs) continue expanding their networks and enhancing profitability through connecting services, passenger demand has become a critical factor in flight connection planning. However, demand is inherently uncertain due to economic cycles, seasonal fluctuations, and external disr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/12/7/574 |
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| Summary: | As low-cost carriers (LCCs) continue expanding their networks and enhancing profitability through connecting services, passenger demand has become a critical factor in flight connection planning. However, demand is inherently uncertain due to economic cycles, seasonal fluctuations, and external disruptions, creating challenges for network design. This study proposes a flight connection planning model tailored to LCC operations that explicitly accounts for demand uncertainty. The model determines the optimal set of connecting itineraries to introduce over the existing network of flights, identifies promising transfer airports, and provides passenger allocation strategies across flights. We apply the model to Spring Airlines’ real-world network to evaluate its effectiveness. Results show that the proposed model outperforms the deterministic benchmark in feasibility and stability under varying demand scenarios. Specifically, under the same constraint of selecting up to 10 transfer airports, our model increases the number of connecting itineraries by 59.5% compared to the deterministic model and achieves a more balanced passenger distribution. Across 10 representative demand scenarios, the average standard deviation of load factors is reduced by 26.1% compared to the deterministic benchmark. Moreover, the deterministic solution yields a 22.9% failure rate for planned connections, while our model maintains 100% feasibility. These findings highlight the model’s value as a resilient, practical decision-support tool for airline planners. |
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| ISSN: | 2226-4310 |