What question did this study set out to answer?

The aim is to develop an optimization framework for aviation maintenance scheduling that addresses dynamic and stochastic challenges.

February 19, 2026Scientific ReportsOpen Access

Dynamic multi-objective aviation maintenance scheduling: an algorithmic framework

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The aim is to develop an optimization framework for aviation maintenance scheduling that addresses dynamic and stochastic challenges.
Introduced a multi-objective optimization model incorporating real-time task arrivals and service times.
Developed a framework combining mathematical modeling and meta-heuristic algorithms with performance guarantees.
Evaluated nine algorithms across 810 experimental configurations.
Achieved statistically significant improvements over baseline scheduling approaches.
Adaptive Tabu Search achieved lowest scheduling cost of $13,072 ± $4544.
Multi-objective methods demonstrated diverse Pareto fronts with a mean hypervolume of 0.0268, dominating the objective space.

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Implication

This framework improves scheduling efficiency in aviation maintenance, indicating significant cost reduction possibilities through advanced algorithms.

Cite This Study

Qi et al. (2026) studied this question.

synapsesocial.com/papers/6996712d80e1323b05ec035e https://doi.org/10.1038/s41598-026-40304-0

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Implication

This framework improves scheduling efficiency in aviation maintenance, indicating significant cost reduction possibilities through advanced algorithms.

Dynamic multi-objective aviation maintenance scheduling: an algorithmic framework

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Discussion

Implication

Cite This Study

Dynamic multi-objective aviation maintenance scheduling: an algorithmic framework

Key Points

Discussion

Implication

Cite This Study

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