Comparative Analysis of Scenario-Adaptive Control Algorithms for Arrival and Departure Operations in Multi-Airport Systems
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| Publicado no: | Aerospace vol. 12, no. 12 (2025), p. 1102-1121 |
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| Autor principal: | |
| Outros Autores: | , |
| Publicado em: |
MDPI AG
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2226-4310 | ||
| 024 | 7 | |a 10.3390/aerospace12121102 |2 doi | |
| 035 | |a 3286238414 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231330 |2 nlm | ||
| 100 | 1 | |a Jiang Furong |u Flight Academy, Civil Aviation University of China, Tianjin 300300, China | |
| 245 | 1 | |a Comparative Analysis of Scenario-Adaptive Control Algorithms for Arrival and Departure Operations in Multi-Airport Systems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a To address the insufficient evaluation of scenario adaptability in the coordinated control of shared waypoints within multi-airport systems, this study proposes two optimization strategies: the Multi-Waypoint Rolling Horizon Control (MWRHC) and the Multi-Waypoint Ant Colony Optimization (MWACO) algorithms. A systematic comparison of their applicability and control performance is conducted. Using empirical data from peak-hour operations in the Yangtze River Delta multi-airport system, the applicability and optimization effectiveness of both algorithms in arrival–departure sequencing are evaluated. The metric “Average Flight Time Improvement” is introduced to quantify and compare the performance of different airports during the optimization process, thereby revealing the operational characteristics of MWRHC and MWACO under varying traffic conditions. The results demonstrate that the MWACO algorithm exhibits superior global optimization capability in high-traffic airport environments, whereas the MWRHC algorithm performs better in local optimization and real-time scheduling under moderate traffic conditions. | |
| 610 | 4 | |a Federal Aviation Administration--FAA | |
| 653 | |a Traffic | ||
| 653 | |a Airports | ||
| 653 | |a Control algorithms | ||
| 653 | |a Flight time | ||
| 653 | |a Global optimization | ||
| 653 | |a Optimization | ||
| 653 | |a Airline scheduling | ||
| 653 | |a Air transportation industry | ||
| 653 | |a Aviation | ||
| 653 | |a Algorithms | ||
| 653 | |a Traffic flow | ||
| 653 | |a Adaptive control | ||
| 653 | |a Waypoints | ||
| 653 | |a Local optimization | ||
| 653 | |a Real time | ||
| 653 | |a Ant colony optimization | ||
| 653 | |a Efficiency | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Transportation terminals | ||
| 700 | 1 | |a Lu, Tingting |u College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China | |
| 700 | 1 | |a Zhang Zhaoning |u College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China | |
| 773 | 0 | |t Aerospace |g vol. 12, no. 12 (2025), p. 1102-1121 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286238414/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286238414/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286238414/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |