Aircraft Routing and Crew Pairing Solutions: Robust Integrated Model Based on Multi-Agent Reinforcement Learning
Every year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two...
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MDPI AG
2025-05-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/5/444 |
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| author | Chengjin Ding Yuzhen Guo Jianlin Jiang Wenbin Wei Weiwei Wu |
| author_facet | Chengjin Ding Yuzhen Guo Jianlin Jiang Wenbin Wei Weiwei Wu |
| author_sort | Chengjin Ding |
| collection | DOAJ |
| description | Every year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two stages—that is, the Aircraft-Routing Problem (ARP) and the Crew-Pairing Problem (CPP). While the ARP and CPP have traditionally been solved sequentially, such an approach fails to capture their interdependencies, often compromising the robustness of aircraft and crew schedules in the face of disruptions. However, existing integrated ARP and CPP models often apply static rules for buffer time allocation, which may result in excessive and ineffective long-buffer connections. To bridge these gaps, we propose a robust integrated ARP and CPP model with two key innovations: (1) the definition of new critical connections (NCCs), which combine structural feasibility with data-driven delay risk; and (2) a spatiotemporal delay-prediction module that quantifies connection vulnerability. The problem is formulated as a sequential decision-making process and solved via a novel multi-agent reinforcement learning algorithm. Numerical results demonstrate that the novel method outperforms prior methods in the literature in terms of solving speed and can also enhance planning robustness. This, in turn, can enhance both operational profitability and passenger satisfaction. |
| format | Article |
| id | doaj-art-9e4a10ee74ce412282ed5ab648537190 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-9e4a10ee74ce412282ed5ab6485371902025-08-20T03:47:52ZengMDPI AGAerospace2226-43102025-05-0112544410.3390/aerospace12050444Aircraft Routing and Crew Pairing Solutions: Robust Integrated Model Based on Multi-Agent Reinforcement LearningChengjin Ding0Yuzhen Guo1Jianlin Jiang2Wenbin Wei3Weiwei Wu4College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaSchool of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaDepartment of Aviation and Technology, College of Engineering, San Jose State University, One Washington Square, San Jose, CA 95192-0061, USACollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaEvery year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two stages—that is, the Aircraft-Routing Problem (ARP) and the Crew-Pairing Problem (CPP). While the ARP and CPP have traditionally been solved sequentially, such an approach fails to capture their interdependencies, often compromising the robustness of aircraft and crew schedules in the face of disruptions. However, existing integrated ARP and CPP models often apply static rules for buffer time allocation, which may result in excessive and ineffective long-buffer connections. To bridge these gaps, we propose a robust integrated ARP and CPP model with two key innovations: (1) the definition of new critical connections (NCCs), which combine structural feasibility with data-driven delay risk; and (2) a spatiotemporal delay-prediction module that quantifies connection vulnerability. The problem is formulated as a sequential decision-making process and solved via a novel multi-agent reinforcement learning algorithm. Numerical results demonstrate that the novel method outperforms prior methods in the literature in terms of solving speed and can also enhance planning robustness. This, in turn, can enhance both operational profitability and passenger satisfaction.https://www.mdpi.com/2226-4310/12/5/444aircraft routingcrew pairingreinforcement learningrobust integrated model |
| spellingShingle | Chengjin Ding Yuzhen Guo Jianlin Jiang Wenbin Wei Weiwei Wu Aircraft Routing and Crew Pairing Solutions: Robust Integrated Model Based on Multi-Agent Reinforcement Learning Aerospace aircraft routing crew pairing reinforcement learning robust integrated model |
| title | Aircraft Routing and Crew Pairing Solutions: Robust Integrated Model Based on Multi-Agent Reinforcement Learning |
| title_full | Aircraft Routing and Crew Pairing Solutions: Robust Integrated Model Based on Multi-Agent Reinforcement Learning |
| title_fullStr | Aircraft Routing and Crew Pairing Solutions: Robust Integrated Model Based on Multi-Agent Reinforcement Learning |
| title_full_unstemmed | Aircraft Routing and Crew Pairing Solutions: Robust Integrated Model Based on Multi-Agent Reinforcement Learning |
| title_short | Aircraft Routing and Crew Pairing Solutions: Robust Integrated Model Based on Multi-Agent Reinforcement Learning |
| title_sort | aircraft routing and crew pairing solutions robust integrated model based on multi agent reinforcement learning |
| topic | aircraft routing crew pairing reinforcement learning robust integrated model |
| url | https://www.mdpi.com/2226-4310/12/5/444 |
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