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A Multi-agent Reinforcement Learning Approach to the Schedule and Aircraft Recovery Problem in Airline Disruption Management

Unexpected events such as airport closures pose significant operational and financial challenges to the operations control centre, requiring decision-makers to make rapid and cost-efficient decisions that minimise the impacts and recover the schedule from these disruptions as soon as possible.
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RLEM: Deep Reinforcement Learning Ensemble Method for Aircraft Recovery Problem

2024 IEEE International Conference on Big Data (BigData)
Dominik Zurek   +4 more
openaire   +1 more source

Integrated Aircraft and Passenger Recovery With Enhancements in Modeling, Solution Algorithm, and Intermodalism

IEEE Transactions on Intelligent Transportation Systems, 2022
Hong Liu, Yu Zhang
exaly  

Fast and Efficient Integer Linear Programming Method for Aircraft Recovery Problem

2025 IEEE International Conference on Big Data (BigData)
Dominik Zurek   +5 more
openaire   +1 more source

A self-adapting memetic algorithm with reinforcement learning for multi-objective aircraft recovery problems

Applied Soft Computing
Xiaoyi Jia   +6 more
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Integrated recovery of aircraft and passengers after airline operation disruption based on a GRASP algorithm

Transportation Research, Part E: Logistics and Transportation Review, 2016
Yuzhen Hu, Kang Zhao, Baoguang Xu
exaly  

A Reinforcement Learning Approach for Initialization of Column Generation with Application to Aircraft Recovery Problem

Proceedings of the 10th International Conference on Cyber Security and Information Engineering
Jingxi Lu, Xiongwen Qian
openaire   +1 more source

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