Abstract
This article presents a retirement analysis model for aircraft fleets. By employing a greedy algorithm, the presented solution is capable of identifying individually weak assets in a fleet of aircraft with inhomogeneous historical utilization. The model forecasts future retirement scenarios employing user-defined decision periods, informed by a cost function, a utility function and demographic inputs to the model. The model satisfies first-order necessary conditions and uses cost minimization, utility maximization or a combination of the 2 as the objective function. This study creates a methodology for applying a greedy algorithm to a military fleet retirement scenario and then uses the United States Air Force A-10 Thunderbolt II fleet for model validation. It is shown that this methodology provides fleet managers with valid retirement options and shows that early retirement decisions substantially impact future fleet cost and utility.
Highlights
Military aircraft fleet managers are responsible for providing strategic capability to their owning command
Aircraft are based around the globe to perform various roles under a variety of operating conditions. As these individual aircraft are flown over time, each one develops a historical utilization profile that is related to its fatigue life expended (Molent et al 2012)
Literature Review A military aircraft fleet retirement methodology must connect the domains of replacement theory, capital asset economics and military operational analysis
Summary
Military aircraft fleet managers are responsible for providing strategic capability to their owning command. The objective of this research was to develop a tool to provide fleet managers with a list of aircraft serial numbers that should be considered for retirement, sorted by precedence and timing. This tool is called the Fleet and Aircraft Retirement Model (FARM). To overcome the limitation of basing forecasts on outdated information, fleet managers can periodically use FARM to update their fleet retirement forecasts, including updated cost and utility data for each iteration. This approach allows fleet managers to alter their utilization levels across a fleet to optimize their retirement scheduling. The conclusions section emphasizes the major findings from this study
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