Hybrid Optimization Approaches for Line Maintenance Scheduling: A 20-Year Review Focused on Turnaround Efficiency in Low-Cost Airlines

Aircraft line maintenance is essential for flight safety, regulatory compliance, and operational efficiency, particularly in the high-frequency, cost-sensitive operations of low-cost carriers (LCCs). As LCCs depend on tight schedules and minimal ground times, optimizing line maintenance during aircraft turnaround has become a critical research focus. This paper provides a systematic review of hybrid optimization approaches developed between 2005 and 2025 to enhance line maintenance scheduling and minimize turnaround time (TAT). Drawing on over 100 academic and industrial sources, the review categorizes contributions across three domains: dynamic programming, heuristic and metaheuristic methods, and machine learning driven predictive analytics. It examines how hybrid models’ combinations of algorithmic approaches have evolved to address complexity, uncertainty, and real-time demands in maintenance environments. Industrial adoption is also assessed, with attention to successful applications, scalability challenges, and limitations in LCC operations. A structured methodology was employed to select and evaluate literature by relevance, rigor, and practical implications. Key performance indicators including TAT reduction, resource utilization, task delay mitigation, and system robustness are synthesized using comparative tables and graphical summaries. The paper highlights research gaps such as limited integration with real-time control systems, underuse of reliability data, and insufficient attention to human factors. The review concludes with recommendations for integrating predictive maintenance data, reinforcement learning, and digital twin technologies into future optimization research. By consolidating fragmented knowledge, this work provides actionable insights for reducing TAT and improving maintenance decision-making in low-cost airline operations.