Crew recovery optimization with deep learning and column generation for sustainable airline operation management
DOI10.1007/s10479-023-05738-zMaRDI QIDQ6658347
Publication date: 8 January 2025
Published in: Annals of Operations Research (Search for Journal in Brave)
optimizationcolumn generationartificial intelligencemachine learningsustainabilityAutoMLsustainable business managementairline crew disruptionscrew recovery problem
Artificial neural networks and deep learning (68T07) Integer programming (90C10) Deterministic scheduling theory in operations research (90B35) Case-oriented studies in operations research (90B90) Theory of organizations, manpower planning in operations research (90B70)
Cites Work
- A survey on deep learning and its applications
- Duty-period-based network model for crew rescheduling in European airlines
- Disruption management in the airline industry-concepts, models and methods
- A network model for airline cabin crew scheduling
- A proactive crew recovery decision support tool for commercial airlines during irregular operations
- Airline disruption management: a literature review and practical challenges
- Machine learning for combinatorial optimization: a methodological tour d'horizon
- On learning and branching: a survey
- Airline crew scheduling from planning to operations
- Reinforcement learning for combinatorial optimization: a survey
- Airline crew recovery
- Airline Scheduling for the Temporary Closure of Airports
- A Column Generation Approach for Large-Scale Aircrew Rostering Problems
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