Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure Scheduling
The intricacies of air traffic departure scheduling, especially when numerous flights are delayed, frequently impede the implementation of automated decision-making for scheduling. To surmount this obstacle, a mathematical model is proposed, and a dynamic simulation framework is designed to tackle t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/9/754 |
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| author | Hong Liu Song Li Fang Sun Wei Fan Wai-Hung Ip Kai-Leung Yung |
| author_facet | Hong Liu Song Li Fang Sun Wei Fan Wai-Hung Ip Kai-Leung Yung |
| author_sort | Hong Liu |
| collection | DOAJ |
| description | The intricacies of air traffic departure scheduling, especially when numerous flights are delayed, frequently impede the implementation of automated decision-making for scheduling. To surmount this obstacle, a mathematical model is proposed, and a dynamic simulation framework is designed to tackle the scheduling dilemma. An optimization control strategy is based on adaptive dynamic programming (ADP), focusing on minimizing the cumulative delay time for a cohort of delayed aircraft amidst congestion. This technique harnesses an approximation of the dynamic programming value function, augmented by reinforcement learning to enhance the approximation and alleviate the computational complexity as the number of flights increases. Comparative analyses with alternative approaches, including the branch and bound algorithm for static conditions and the first-come, first-served (FCFS) algorithm for routine scenarios, are conducted. Moreover, perturbation simulations of ADP parameters validate the method’s robustness and efficacy. ADP, when integrated with reinforcement learning, demonstrates time efficiency and reliability, positioning it as a viable solution for decision-making in departure management systems. |
| format | Article |
| id | doaj-art-811e95e2fbb64995976cfaeca5b0ffec |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-811e95e2fbb64995976cfaeca5b0ffec2025-08-20T01:56:06ZengMDPI AGAerospace2226-43102024-09-0111975410.3390/aerospace11090754Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure SchedulingHong Liu0Song Li1Fang Sun2Wei Fan3Wai-Hung Ip4Kai-Leung Yung5Department of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, ChinaState Key Laboratory of Air Traffic Management System, Nanjing 210014, ChinaDepartment of Science, Civil Aviation University of China, Tianjin 300300, ChinaDepartment of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, ChinaDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaThe intricacies of air traffic departure scheduling, especially when numerous flights are delayed, frequently impede the implementation of automated decision-making for scheduling. To surmount this obstacle, a mathematical model is proposed, and a dynamic simulation framework is designed to tackle the scheduling dilemma. An optimization control strategy is based on adaptive dynamic programming (ADP), focusing on minimizing the cumulative delay time for a cohort of delayed aircraft amidst congestion. This technique harnesses an approximation of the dynamic programming value function, augmented by reinforcement learning to enhance the approximation and alleviate the computational complexity as the number of flights increases. Comparative analyses with alternative approaches, including the branch and bound algorithm for static conditions and the first-come, first-served (FCFS) algorithm for routine scenarios, are conducted. Moreover, perturbation simulations of ADP parameters validate the method’s robustness and efficacy. ADP, when integrated with reinforcement learning, demonstrates time efficiency and reliability, positioning it as a viable solution for decision-making in departure management systems.https://www.mdpi.com/2226-4310/11/9/754adaptive dynamic programmingdeparture schedulingconstrained integer optimizationsystem robustness |
| spellingShingle | Hong Liu Song Li Fang Sun Wei Fan Wai-Hung Ip Kai-Leung Yung Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure Scheduling Aerospace adaptive dynamic programming departure scheduling constrained integer optimization system robustness |
| title | Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure Scheduling |
| title_full | Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure Scheduling |
| title_fullStr | Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure Scheduling |
| title_full_unstemmed | Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure Scheduling |
| title_short | Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure Scheduling |
| title_sort | adaptive dynamic programming with reinforcement learning on optimization of flight departure scheduling |
| topic | adaptive dynamic programming departure scheduling constrained integer optimization system robustness |
| url | https://www.mdpi.com/2226-4310/11/9/754 |
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