Genetic Algorithm and Mathematical Modelling for Integrated Schedule Design and Fleet Assignment at a Mega-Hub
Airline networks are becoming increasingly complex, particularly at mega-hub airports characterized by high transit volumes. Effective schedule design and fleet assignment are critical for an airline, as they directly influence passenger connectivity and profitability. This study addresses the chall...
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MDPI AG
2025-06-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/6/545 |
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| author | Melis Tan Tacoglu Mustafa Arslan Ornek Yigit Kazancoglu |
| author_facet | Melis Tan Tacoglu Mustafa Arslan Ornek Yigit Kazancoglu |
| author_sort | Melis Tan Tacoglu |
| collection | DOAJ |
| description | Airline networks are becoming increasingly complex, particularly at mega-hub airports characterized by high transit volumes. Effective schedule design and fleet assignment are critical for an airline, as they directly influence passenger connectivity and profitability. This study addresses the challenge of introducing a new route from a mega-hub to a new destination, while maintaining the existing flight network and leveraging arrivals from spoke airports to ensure connectivity. First, a mixed-integer nonlinear mathematical model was formulated to produce a global optimal solution at a lower time granularity, but it became computationally intractable at higher granularities due to the exponential growth in constraints and variables. Second, a genetic algorithm (GA) was employed to demonstrate scalability and flexibility, delivering near-optimal, high-granularity schedules with significantly reduced computational time. Empirical validation using real-world data from 37 spoke airports revealed that, while the exact model minimized waiting times and maximized profit at lower granularity, the GA provided nearly comparable profit at higher granularity. These findings guide airline managers seeking to optimize passenger connectivity and cost efficiency in competitive global markets. |
| format | Article |
| id | doaj-art-b724d63e5ee84e8fa48f816f1070f16b |
| institution | Matheson Library |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-b724d63e5ee84e8fa48f816f1070f16b2025-06-25T13:19:33ZengMDPI AGAerospace2226-43102025-06-0112654510.3390/aerospace12060545Genetic Algorithm and Mathematical Modelling for Integrated Schedule Design and Fleet Assignment at a Mega-HubMelis Tan Tacoglu0Mustafa Arslan Ornek1Yigit Kazancoglu2Graduate School, Yasar University, İzmir 35100, TürkiyeDepartment of Industrial Engineering; Yasar University, İzmir 35100, TürkiyeDepartment of Logistics Management; Yasar University, İzmir 35100, TürkiyeAirline networks are becoming increasingly complex, particularly at mega-hub airports characterized by high transit volumes. Effective schedule design and fleet assignment are critical for an airline, as they directly influence passenger connectivity and profitability. This study addresses the challenge of introducing a new route from a mega-hub to a new destination, while maintaining the existing flight network and leveraging arrivals from spoke airports to ensure connectivity. First, a mixed-integer nonlinear mathematical model was formulated to produce a global optimal solution at a lower time granularity, but it became computationally intractable at higher granularities due to the exponential growth in constraints and variables. Second, a genetic algorithm (GA) was employed to demonstrate scalability and flexibility, delivering near-optimal, high-granularity schedules with significantly reduced computational time. Empirical validation using real-world data from 37 spoke airports revealed that, while the exact model minimized waiting times and maximized profit at lower granularity, the GA provided nearly comparable profit at higher granularity. These findings guide airline managers seeking to optimize passenger connectivity and cost efficiency in competitive global markets.https://www.mdpi.com/2226-4310/12/6/545new route schedulingschedule designfleet assignmentgenetic algorithmmathematical modeling |
| spellingShingle | Melis Tan Tacoglu Mustafa Arslan Ornek Yigit Kazancoglu Genetic Algorithm and Mathematical Modelling for Integrated Schedule Design and Fleet Assignment at a Mega-Hub Aerospace new route scheduling schedule design fleet assignment genetic algorithm mathematical modeling |
| title | Genetic Algorithm and Mathematical Modelling for Integrated Schedule Design and Fleet Assignment at a Mega-Hub |
| title_full | Genetic Algorithm and Mathematical Modelling for Integrated Schedule Design and Fleet Assignment at a Mega-Hub |
| title_fullStr | Genetic Algorithm and Mathematical Modelling for Integrated Schedule Design and Fleet Assignment at a Mega-Hub |
| title_full_unstemmed | Genetic Algorithm and Mathematical Modelling for Integrated Schedule Design and Fleet Assignment at a Mega-Hub |
| title_short | Genetic Algorithm and Mathematical Modelling for Integrated Schedule Design and Fleet Assignment at a Mega-Hub |
| title_sort | genetic algorithm and mathematical modelling for integrated schedule design and fleet assignment at a mega hub |
| topic | new route scheduling schedule design fleet assignment genetic algorithm mathematical modeling |
| url | https://www.mdpi.com/2226-4310/12/6/545 |
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