Simulation-Based Two-Stage Scheduling Optimization Method for Carrier-Based Aircraft Launch and Departure Operations
The scheduling of carrier-based aircraft departure operations is subject to stringent temporal, spatial, and resource constraints. Conventional approaches struggle to yield exact solutions or provide a comprehensive mathematical description of this complex, dynamic process. This study proposes a sim...
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MDPI AG
2025-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/7/662 |
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| author | Jue Liu Nengjian Wang |
| author_facet | Jue Liu Nengjian Wang |
| author_sort | Jue Liu |
| collection | DOAJ |
| description | The scheduling of carrier-based aircraft departure operations is subject to stringent temporal, spatial, and resource constraints. Conventional approaches struggle to yield exact solutions or provide a comprehensive mathematical description of this complex, dynamic process. This study proposes a simulation-based optimization method, establishing a high-fidelity simulation model for aircraft departure scheduling. To address the coupled challenges of path planning under spatial constraints and station matching/sequencing under operational constraints, we developed (1) a deep reinforcement learning (DRL)-based path planning algorithm (AAE-SAC), and (2) an enhanced particle swarm optimization (PSO) algorithm (LTA-HPSO). This integrated two-stage framework, termed LTA-HPSO + AAE-SAC, facilitates efficient, collision-free departure scheduling optimization. Simulation experiments across varying sortie scales were conducted to validate the framework’s effectiveness and robustness. Notably, for a complex scenario involving 24 aircraft with diverse priorities and stringent spatial constraints, LTA-HPSO + AAE-SAC achieved an average solution time of 185.19 s, reducing scheduling time by 26.18% and 49.54% compared to benchmark algorithms (PSO + Heuristic and PSO + SAC, respectively). The proposed LTA-HPSO + AAE-SAC framework significantly enhances the quality and robustness of carrier-based aircraft departure scheduling. |
| format | Article |
| id | doaj-art-61394b3bdf8b4be1b4f0c7df54c26b80 |
| institution | Matheson Library |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Entropy |
| spelling | doaj-art-61394b3bdf8b4be1b4f0c7df54c26b802025-07-25T13:22:10ZengMDPI AGEntropy1099-43002025-06-0127766210.3390/e27070662Simulation-Based Two-Stage Scheduling Optimization Method for Carrier-Based Aircraft Launch and Departure OperationsJue Liu0Nengjian Wang1College of Mechanical and Electrical Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin 150001, ChinaThe scheduling of carrier-based aircraft departure operations is subject to stringent temporal, spatial, and resource constraints. Conventional approaches struggle to yield exact solutions or provide a comprehensive mathematical description of this complex, dynamic process. This study proposes a simulation-based optimization method, establishing a high-fidelity simulation model for aircraft departure scheduling. To address the coupled challenges of path planning under spatial constraints and station matching/sequencing under operational constraints, we developed (1) a deep reinforcement learning (DRL)-based path planning algorithm (AAE-SAC), and (2) an enhanced particle swarm optimization (PSO) algorithm (LTA-HPSO). This integrated two-stage framework, termed LTA-HPSO + AAE-SAC, facilitates efficient, collision-free departure scheduling optimization. Simulation experiments across varying sortie scales were conducted to validate the framework’s effectiveness and robustness. Notably, for a complex scenario involving 24 aircraft with diverse priorities and stringent spatial constraints, LTA-HPSO + AAE-SAC achieved an average solution time of 185.19 s, reducing scheduling time by 26.18% and 49.54% compared to benchmark algorithms (PSO + Heuristic and PSO + SAC, respectively). The proposed LTA-HPSO + AAE-SAC framework significantly enhances the quality and robustness of carrier-based aircraft departure scheduling.https://www.mdpi.com/1099-4300/27/7/662simulationcarrier-based aircraftscheduling optimizationalgorithm improvementdeparture operation |
| spellingShingle | Jue Liu Nengjian Wang Simulation-Based Two-Stage Scheduling Optimization Method for Carrier-Based Aircraft Launch and Departure Operations Entropy simulation carrier-based aircraft scheduling optimization algorithm improvement departure operation |
| title | Simulation-Based Two-Stage Scheduling Optimization Method for Carrier-Based Aircraft Launch and Departure Operations |
| title_full | Simulation-Based Two-Stage Scheduling Optimization Method for Carrier-Based Aircraft Launch and Departure Operations |
| title_fullStr | Simulation-Based Two-Stage Scheduling Optimization Method for Carrier-Based Aircraft Launch and Departure Operations |
| title_full_unstemmed | Simulation-Based Two-Stage Scheduling Optimization Method for Carrier-Based Aircraft Launch and Departure Operations |
| title_short | Simulation-Based Two-Stage Scheduling Optimization Method for Carrier-Based Aircraft Launch and Departure Operations |
| title_sort | simulation based two stage scheduling optimization method for carrier based aircraft launch and departure operations |
| topic | simulation carrier-based aircraft scheduling optimization algorithm improvement departure operation |
| url | https://www.mdpi.com/1099-4300/27/7/662 |
| work_keys_str_mv | AT jueliu simulationbasedtwostageschedulingoptimizationmethodforcarrierbasedaircraftlaunchanddepartureoperations AT nengjianwang simulationbasedtwostageschedulingoptimizationmethodforcarrierbasedaircraftlaunchanddepartureoperations |