Mixed-integer optimization for multi-year military aircraft fleet management
Abstract
While existing strategies for Flight and Maintenance Planning for the defense sector generally address idealized conditions, real-world planning often involve non-nominal initial fleet states and complex inspection schemes. To address these challenges, we propose a multi-year planning strategy that maximizes long-term fleet availability. The formulation incorporates multiple competing objectives capturing the tight coupling between aircraft usage and maintenance, while enforcing cyclic inspection requirements and limited maintenance-dock capacity. To enhance practical applicability, we introduce three complementary strategies: a receding-horizon approach to reduce computational burden, a Bayesian Optimization routine to automatically tune cost-function coefficients, and an aircraft-specific weighting scheme that adapts model priorities based on fleet-condition statistics. Results on three realistic benchmark scenarios demonstrate the effectiveness of the approach.
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BibTeX
@misc{vescovi2026mixedinteger,
title = {{Mixed-integer optimization for multi-year military aircraft fleet management}},
author = {Matteo Vescovi and Raffaele Giuseppe Cestari and Roberto Valdambrini and Andrea Mercurio and Valentina Breschi and Mara Tanelli},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.11710},
eprint = {2609.11710},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.11710},
}
RIS
TY - UNPB
TI - Mixed-integer optimization for multi-year military aircraft fleet management
AU - Matteo Vescovi
AU - Raffaele Giuseppe Cestari
AU - Roberto Valdambrini
AU - Andrea Mercurio
AU - Valentina Breschi
AU - Mara Tanelli
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.11710
UR - https://arxiv.org/abs/2609.11710
AB - While existing strategies for Flight and Maintenance Planning for the defense sector generally address idealized conditions, real-world planning often involve non-nominal initial fleet states and complex inspection schemes. To address these challenges, we propose a multi-year planning strategy that maximizes long-term fleet availability. The formulation incorporates multiple competing objectives capturing the tight coupling between aircraft usage and maintenance, while enforcing cyclic inspection requirements and limited maintenance-dock capacity. To enhance practical applicability, we introduce three complementary strategies: a receding-horizon approach to reduce computational burden, a Bayesian Optimization routine to automatically tune cost-function coefficients, and an aircraft-specific weighting scheme that adapts model priorities based on fleet-condition statistics. Results on three realistic benchmark scenarios demonstrate the effectiveness of the approach.
ER -