Executive Summary
This paper presents a multi-objective, mixed-integer optimization model for multi-year military aircraft fleet management, aiming to maximize long-term fleet availability. It addresses the computational complexity of long-term planning through a receding horizon strategy and uses Bayesian Optimization for automatic tuning of cost function weights, enhancing practical applicability and efficiency.
Why It Matters
Optimizing military aircraft fleet availability and maintenance planning is crucial for operational readiness and cost-efficiency in defense operations.
Key Takeaways
- A multi-year, multi-objective mixed-integer optimization model is proposed to maximize military aircraft fleet availability by balancing usage and maintenance, considering cyclic inspections and limited dock capacity.
Source evidence · PDF page 1
To address these challenges, we propose a multi-year planning strategy that maximizes long-term fleet availability. The formulation incorporates multi- ple competing objectives capturing the tight coupling between aircraft us- age and maintenance, while enforcing cyclic inspection requirements and limited maintenance-dock capacity.
- The model incorporates a receding horizon strategy to manage computational complexity for long-term planning and a Bayesian Optimization routine to automatically tune cost function coefficients, improving practical applicability.
Source evidence · PDF page 5
To address challenging initial fleet conditions and the computational burden of long-term multi- objective optimization, we introduce three practical enhancements. First, we adopt a receding horizon strategy (Sethi and Sorger, 1991; Sahin et al., 2013) to reduce computational cost while avoiding the myopic behaviour of solving a MIP separately for each year.
- Aircraft-specific weighting schemes are introduced to adapt model priorities based on fleet condition statistics, effectively mitigating inspection queues and enhancing overall fleet efficiency, especially for critical or new fleets.
Source evidence · PDF page 26
Results on a fleet with a critical initial scaling highlight the advantages of automatic tuning over manual selection and its potential to reduce user burden. Fi- nally, we propose custom penalty coefficients’ definitions to handle cases where multiple aircraft share similar residual flight hours, mitigating inspec- tion queues and improving overall fleet efficiency.
Strategic Relevance
This research directly supports military operational readiness by providing advanced tools for managing complex aircraft fleets, ensuring high availability and efficient maintenance, which are critical for sustained air power projection and defense capabilities.