Flight, aircraft, and crew integrated recovery policies for airlines - A deep reinforcement learning approach-Reference-Cited by-同舟云学术

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Flight, aircraft, and crew integrated recovery policies for airlines - A deep reinforcement learning approach

Published:2025-01 Issue: Volume:160 Page:245-258
ISSN:0967-070X
Container-title:Transport Policy
language:en
Short-container-title:Transport Policy

Author:

Wang Qi^ORCID,Mao Jianing^ORCID,Wen Xin^ORCID,Wallace Stein W.,Deveci Muhammet

Publisher

Elsevier BV

Reference48 articles.

1. Aircraft rescheduling with cruise speed control;Aktürk;Oper. Res.,2014

2. Framework for Exact Solutions and Heuristics for Approximate Solutions to Airlines' Irregular Operations Control Aircraft Routing Problem;Argüello,1997

3. A GRASP for aircraft routing in response to groundings and delays;Argüello;J. Combin. Optim.,1997

4. Flight network-based approach for integrated airline recovery with cruise speed control;Arıkan;Transport. Sci.,2017

5. A duty based approach in solving the aircrew recovery problem;Chang;J. Air Transport. Manag.,2012

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. ADMM-based augmented Lagrangian methods for robust aircraft recovery problem considering connection time, resource capacity and maintenance flexibility;Transportation Research Part E: Logistics and Transportation Review;2025-09

2. RecovAir: Model-driven airline scheduling tool for disruption recovery;Frontiers in Built Environment;2025-05-12

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