SmartBasing provides fleet managers tools with which to manage their end-of-life aircraft fleets. The principles of SmartBasing include reassigning aircraft to different bases and assigning aircraft to a new mix of mission types to actively manage the remaining useful lifetime of each aircraft in a fleet. This paper employs a single case study aircraft to validate the SmartBasing approach for a dynamic strategy for aircraft retirement. The United States Air Force’s A-10 Thunderbolt II was used for validation, because it is an aging aircraft fleet that experienced a partial fleet retirement in 2013. The efficacy of the SmartBasing principles was tested using the aircraft retired in 2013 by altering usage patterns and basing locations in the years leading to the 2013 retirement. It was shown that SmartBasing would have been a valid technique for managing the A-10 fleet prior to its partial retirement. Better aircraft utilization planning could have expended more residual aircraft lifetime prior to retirement, resulting in savings of more than 1.88 full aircraft lifetimes or over 83 million USD in aircraft acquisition costs.

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Military aircraft retirements are an afterthought for many lifecycle planners. More active management of end-of-life fleets can yield increased confidence in fleet capability and retirement timelines. This work provides fleet managers with a tool to manage remaining aircraft flight hours to yield a desired fleet retirement pattern. It solves an equivalent flight hour minimization problem using a mixed-integer linear programming model for a military aircraft fleet having a network with basing and mission type constraints. The model minimizes differences in remaining equivalent flight hours for individual aircraft in future years, thereby allowing a fleet manager to alter the timeline for retirement of individual aircraft. A relocation cost is applied to discourage excessive, costly aircraft relocations. The United States Air Force A-10 Thunderbolt II aircraft is used as a case study while disruptions such as deployments are modeled to show the methodology's robustness. This work proves that a fleet of aircraft with dissimilar utilization histories and varying amounts of remaining useful lifetime can be actively managed to change the time at which individual aircraft are ready for retirement. The benefit to fleet managers is the ability to extract additional lifetime out of their aircraft prior to retirement.

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The U.S. Department of Defense’s largest acquisition program in history, the F-35 Joint Strike Fighter, is a multinational defense program with nine partner nations. As of January 2018, the program’s 260+ flying aircraft have flown over 115,000 flight hours at 14 military installations around the globe. The aircraft’s flight envelope is proven, munitions are cleared for carriage and the aircraft has reached operational capability. One decade prior, AA-1 was the sole flying F-35 struggling to achieve test points because of immature hardware and software. AA-1 and subsequent developmental test aircraft flights were managed by a control room, staffed by a team of flight test engineers. The evolution from requiring 40 control room engineers for a flight to today’s state provides countless lessons learned. This paper encapsulates the flight test period of the F-35A from 2009-2012 and provides practical control room lessons learned from the mistakes and successes made during developmental testing. It is shown that the flight test engineers made advances in control room procedures to accommodate the complexities of the F-35A systems and were thus able to meet the demands of the test program schedule.@en

The purpose of this work is as follows. Military aircraft are enormous investments for a nation. The systems lifecycle for aircraft spans decades wherein aging effects increase maintenance and operations costs over time. At some point, the deterioration of a fleet of aircraft erodes the capability of those assets below an acceptable threshold, thus triggering retirement planning by a military. Questions arise about how to retire a fleet, including how many aircraft should be retired, when those aircraft should be retired and which aircraft should be chosen. There are few military aircraft fleets that are retired each year, and even fewer managers who understand the aircraft retirement puzzle. This work addresses these questions. The purpose was to provide fleet managers with a comprehensive framework to guide decision-making, as well as to build tools and a standard guidance framework for fleet managers to implement.

In terms of methodology, in the absence of directly applicable existing research in this field, fleet management concepts and modelling approaches were studied in related fields and then applied to the military fleet retirement problem. The vital first approach to the problem required the baselining of military aircraft fleets given structural loading data and utilization histories. Database analysis and trending algorithms were written to draw correlations between existing data and structural fatigue effects. This work then implemented a greedy algorithm model to solve the individual aircraft retirement scheme. That led to a mixed-integer linear programming approach to optimize a fleet utilization and rotation model. Combined, these methods provided concrete steps for the fleet retirement decision framework, which followed established methods for designing a decision support framework. Throughout the work, a consistent case study fleet (United States Air Force’s A 10 Thunderbolt II) was utilized to provide validation of the methods, while secondary case studies and validation techniques were employed to test applicability of the methods to other military aircraft fleets and other capital asset types.

In terms of concrete research results from the work carried out, this dissertation discovered that a framework for military aircraft fleet retirement decisions was a needed contribution to the field. In the process of building that framework, other valuable results were obtained. It was found that aircraft utilization information could be correlated to cyclic loading data on an individual aircraft level. This revealed patterns in aircraft fleets showing which mission types and basing locations either increased or decreased structural degradation. Using that information led to the result that a fleet manager could determine which aircraft to retire prior to others while optimizing an objective function related to fleet cost, fleet utility or the ratio thereof. It was also found that a fleet manager could selectively utilize individual aircraft at particular bases flying particular missions to prolong or hasten the structural degradation of those aircraft. This led to the result that a fleet manager could therefore forecast retirement dates for an entire fleet, subpopulations within that fleet or individual assets.

From the research carried out, it is emphatically concluded that the results imply that a fleet manager beginning with only aircraft usage data can actively manage a fleet of aircraft to extract residual value from the fleet prior to retirement. This work showed that resource allocation could be improved by utilizing a mixed integer linear program to schedule asset retirements. Further, this work illustrated how a management strategy could impact future usage levels in a way to extend useful lifetime. With a capital asset as critical to national defense and as expensive to acquire, operate and retire as military aircraft, focusing on the end-of-life phase of the systems lifecycle not only promotes forward thinking but also provides potential cost savings. This work’s limitations included its focus on military aircraft instead of all capital assets and that the methods were not implemented in an actual fleet environment. This dissertation demonstrated that a flexible framework with core modelling elements is a tool capable of solving the problem of aircraft fleet retirement decisions. Fleet managers both military and otherwise should investigate the applicability of the methods and findings in this dissertation to their own challenges. Future research must include application of the methods to an actual operating fleet. Also, the methods should be applied to other capital asset classes including military equipment and commercial equipment.
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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.@en

Individual aircraft tracking data can be used by aircraft fleet managers to detect patterns in historical usage as a means to aid aging aircraft decision-making. This work tackles two aspects of applying these tracking data: investigating retirement patterns and assessing how base assignment can impact usage. The A-10, C-17 and F-35 acquisition schedules were analyzed to set the expectation for retirement forecasting. Then three types of retirement patterns were assessed - the Cliff, Multi-Step and Ramp - and the merits of each are presented. Equivalent flight hours were used as an approximation for fatigue life expended in the analysis of retirement patterns in tracking data. A candidate set of tracking data was investigated to uncover base usage variations across a network. The dissimilar mission type requirements at each base led to unique loading profiles for aircraft at each of the bases in the network. These findings lead to the natural conclusion that base assignment can be used as a way to modify the loading accumulation on individual tail numbers and across a fleet.@en

It is well known that aircraft fleets are aging alongside rising operations and support costs. Logisticians and fleet managers who better understand the milestones and timeline of an aging fleet can recognise potential savings. This paper outlines generalised milestones germane to military aircraft fleets and then discusses the causes that lead to retirement motivations. Then this paper develops a utility per cost metric for aging aircraft fleet comparison as a means for determining when to retire a fleet. It is shown that utility per cost is a pragmatic metric for gauging the desirability of an existing fleet because of naturally occurring zones. Historical data from the US Air Force's fleet are used to validate the existence of these zones. Lastly, this work highlights the need for increased vigilance during the waning years of a fleet's lifecycle and discusses the intricacies of asset divestment planning.@en

Military attack aircraft are susceptible to the harmful effects of widespread fatigue damage caused by cyclic loading of structural components, which leads to airframe retirement. Modern structural health monitoring techniques use a multitude of sensors and high data collection rates. Some legacy airframes, which are most susceptible to fatigue damage due to their age, possess a counting accelerometer technology with few sensors and low data capture rates. The data provided by these 40-year old devices are crucial to understanding fleet health and can be used to extend structural lifetime for aging aircraft. Existing literature has addressed counting accelerometer usefulness, but a profound three-decade gap in research has led to a chasm between the current wealth of available data and tool development for utilizing those data. This research uses 11 years of A-10 Thunderbolt II counting accelerometer data to prove that mission type, mission duration and aircraft type correlate to aircraft loading patterns. It is shown that a mission type model can therefore influence fleet management strategies and the structural lifetime extension for aging aircraft@en

Military aircraft fleets are continuing to age despite increased structural integrity concerns and rising maintenance costs. Aircraft are not being replaced or retired in large numbers but are instead having their lives extended beyond their original design service lives. Because aging aircraft cost more to maintain, this additional burden on air forces is a forcing function for smarter approaches to enhanced structural health monitoring. As data recorder technology has improved and recording capacity has increased, structural health monitoring tools have become more important in understanding aircraft life. Accrued historical data present opportunities for end-of-life fleet optimization. This paper provides a thorough review of the aging aircraft problem and suggests a direction for future end-of-life fleet optimization research. The suggestions include the alteration of aircraft utilization, optimization for aircraft basing and the prediction of structural fatigue, all of which can enable the realization of fleet-wide cost savings.@en