Aircraft fleet managers lack tools to aid decision-making for fleets nearing retirement, which leads to rushed and ill-informed decisions. Accordingly, aging aircraft fleets are underutilized and fleets can be retired before their useful lifetime has been expended. A decision support framework is proposed to solve the aging military aircraft retirement problem. It integrates four steps for fleet managers to simplify the decision-making process: (i) Understanding the structural toll caused by utilization, (ii) Recognizing the indicators that predispose a fleet for retirement, (iii) Determining an optimal fleet size and choosing which aircraft to retire and (iv) Optimizing end-of-life usage prior to retirement. An example using a sample military fleet is used to illustrate the effectiveness of the decision support framework, integrating both computational results and manager judgement. Fleet managers were used to validate the concepts in the framework and their opinions are presented herein. It is shown that fleet managers can utilize a decision support framework to positively impact their decision-making for full-spectrum aging aircraft retirement decisions.

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Aircraft dispatch involves determining the optimal dispatch option when an aircraft experiences an unexpected failure. Currently, maintenance technicians at the apron have limited access to support information and finding the right information in extensive maintenance manuals is a time-consuming task, often leading to technically induced delays. This paper introduces a novel web-based prototype decision support system to aid technicians during aircraft dispatch decision-making and subsequent maintenance execution. A system architecture for real-time dispatch decision support is established and implemented. The developed system is evaluated through a case study in an operational environment by licensed maintenance technicians. The system fully automates information retrieval from multiple data sources, performs alternative identification and evaluation for a given fault message, and provides the technician with on-site access to relevant information, including the related maintenance tasks. The case study indicates a potential time saving of up to 98% per dispatch decision. Moreover, it enables digitalization of the—currently mostly paper-based—dispatch decision process, thereby reducing logistics and paper waste. The prototype is the first to provide operational decision support in the aircraft maintenance domain and addresses the lack of correlation between theory and practice often found in decision support systems research by providing a representative case study. The developed custom parser for SGML-based documents enables efficient identification and extraction of relevant information, vastly contributing to the overall reduction of the decision time.

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This paper proposes an analytical model that uses historical damage dimension data to deduce physical impactor characteristics (size and energy) that has caused a certain resulting damage. Maintenance tasks occur in operations due to impact, however the source of the damage caused in the event remains in most cases unknown. Consequently, by inferring what has caused a certain type of damage from the distribution of the damage type and severity relative to impactor types, maintainers can be better prepared in terms of what to expect from a given impactor source. The developed model introduces a novel transition deformation region between the local deformation and the global plate deflection, allowing for fast and accurate predictions of the impact event. Using the known aluminium structural properties and damage dimensions, the damage data is converted into impactor data. The model is applied in a case study using 120 fuselage dent damages dimensions (length, width, and depth) from a Boeing 777 fleet. The results show that the model deduces impactor characteristics for 94% of the considered damages, ranging up to 240 J and 110 mm for impactor energy and radius respectively.

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This paper presents the development of a multilevel optimization framework for the design and selection of departure routes, and the distribution of aircraft movements among these routes, while taking the sequence and separation requirements for aircraft on runways and along selected routes into account. The main aim of the framework is to minimize aircraft noise impact on communities around an airport, and the associated fuel consumption. The proposed framework features two consecutive steps. In the first step, for each given Standard Instrument Departure (SID), multi-objective trajectory optimization is utilized to generate a comprehensive set of possible alternative routes. The obtained set is subsequently used as input for the optimization problem in the second step. In this step, the selection of routes for each SID and the distribution of aircraft movements among these routes are optimized simultaneously. To ensure the feasibility of optimized solutions for an entire operational day, the sequence and separation requirements for aircraft on runways and along selected routes are included in this second phase. In order to address these issues, three novel techniques are developed and added to a previously developed multilevel optimization framework, viz., a runway assignment model, a conflict detection algorithm, and a rerouting technique. The proposed framework is applied to a realistic case study at Amsterdam Airport Schiphol in the Netherlands, in which 599 departure flights and 13 different SIDs are considered. The optimization results show that the proposed model can offer conflict-free solutions, one of which can lead to a reduction in the number of people annoyed of up to 21%, and a reduction in fuel consumption of 8% relative to the reference case solution.

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Prognostic and health management (PHM) describes a set of capabilities that enable to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL), leading to the effective and efficient maintenance and operation of assets such as aircraft. Prior research has considered the methodological factors of PHM system design, but typically, only one or a few aspects are addressed. For example, several studies address system engineering (SE) principles for application towards PHM design methodology, and a concept of requirements from a theoretical standpoint, while other papers present requirement specification and flow-down approaches for PHM systems. However, the state of the art lacks a systematic methodology that formulates all aspects of designing and comprehensively engineering a PHM system. Meanwhile, the process and specific implementation of capturing stakeholders’ expectations and requirements are usually lacking details. To overcome these drawbacks, this paper proposes a stakeholder-oriented design methodology for developing a PHM system from a systems engineering perspective, contributing to a consistent and reusable representation of the design. Further, it emphasizes the process and deployment of stakeholder expectations definition in detail, involving the steps of identifying stakeholders, capture their expectations/requirements, and stakeholder and requirement analysis. Two case studies illustrate the applicability of the proposed methodology. The proposed stakeholder-oriented design methodology enables the integration of the bespoke main tasks to design a PHM system, in which sufficient stakeholder involvement and consideration of their interests can lead to more precise and better design information. Moreover, the methodology comprehensively covers the aspects of traceability, consistency, and reusability to capture and define stakeholders and their expectations for a successful design.

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This paper presents a practical dynamic programming based methodology to optimize the long-term maintenance check schedule for a fleet of heterogeneous aircraft. It is the first time that the long-term aircraft maintenance check schedule is optimized, integrating different check types in a single schedule solution. The proposed methodology aims at minimizing the wasted interval between checks. By achieving this goal, one is also reducing the number of checks over time, increasing aircraft availability and, therefore, reducing maintenance costs, while respecting safety regulations. The model formulation takes aircraft type, status, maintenance capacity, and other operational constraints into consideration. We also validate and demonstrate the proposed methodology using fleet maintenance data from a European airline. The outcomes show that, when compared with the current practice, the number of maintenance checks can be reduced by around 7% over a period of 4 years, while computation time is less than 15 minutes. This could result in saving worth $1.1M–$3.4M in maintenance costs for a fleet of about 40 aircraft and generating more than $9.8M of revenue due to higher aircraft availability.

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The present study proposes an economic indicator to support the evaluation of aircraft End of Life (EoL) strategies in view of the increasing demand with regards to aircraft decommissioning. This indicator can be used to evaluate the economic performance and to facilitate the trade-off studies among different strategies. First, Disposal and Recycle (D&R) scenarios related to stakeholders are investigated to identify the core concepts for the economic evaluation. Next, we extracted the aircraft D&R process from various real-life practices. In order to obtain the economic measure for the engineering process, a method of estimating the D&R cost and values are developed by integrating product, process and cost properties. This analysis is demonstrated on an averaged data set and two EoL aircraft cases. In addition, sensitivity analysis is performed to evaluate the impact of the D&R cost, residual value, and salvage value. Results show that the disassembly and dismantling of an aircraft engine possesses relatively more economic gains than that for the aircraft. The main factors influencing the proposed D&R economic indicator are the salvage value and D&R cost for economically efficient D&R cases. In addition, delaying the disposal and recycle process for EoL aircraft can lead to economically unfavorable solutions. The economic indicator combined with the evaluation methods is widely applicable for evaluations of engineering products EoL solutions, and implies a significant contribution of this research to decision making for such complex systems in terms sustainable policy.

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Prognostic and Health Management (PHM) systems support aircraft maintenance through the provision of diagnostic and prognostic capabilities, leveraging the increased availability of sensor data on modern aircraft. Diagnostics provide the functionalities of failure detection and isolation, whereas prognostics can predict the remaining useful life (RUL) of the system. In literature, PHM technologies have been studied from different perspectives, covering various aims such as improving aircraft system reliability, availability, safety and reducing the maintenance cost. From a design perspective, several conceptual formulations of design methodologies are available, enabling a set of PHM system architectures based on different frameworks and the derivation of system requirements. However, a systematic methodology towards a consistent definition of PHM architectures has not been well established. The characteristics of architectures have not been dealt with in depth. To address these gaps, this paper presents a systematic methodology for PHM architecture definition to ensure a more complete and consistent design during the development phase of the product lifecycle. Moreover, a generic PHM architecture in accordance with this systematic methodology is proposed in this article. A case study is conducted to verify and validate the architecture, ensuring it meets the requirements for a correct and complete representation of PHM characteristics.

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Aircraft maintenance has been further developed with predictive maintenance instead of solely condition-based maintenance. Prognostics and health management (PHM) with advanced technologies can utilize real-time and historical health state information to provide actionable information, enabling predictive maintenance decision-making. In this case, the methodology of how to design the PHM systems is an issue to be faced. The state of the art has provided several conceptual design methodologies and associated methods to support the conceptual requirements development of PHM systems. However, there is no rigorous process available for requirements definition. Existing options for requirements derivation are lacking details, which restricting PHM system design and development. This constitutes a major drawback and hurdle towards the successful design of PHM systems in practice. This paper consequently proposes a methodology for the systematic derivation of system requirements towards PHM system development. Besides, this methodology defines detailed processes for requirements definition, and positions mean through which various categories of requirements can be derived through appropriate analyses in detail. Sequences of interoperability requirements categories and associated flow-down perspectives are identified. To evaluate the applicability, this paper undertakes the case study of requirements definition for a generic PHM system, which provides a comprehensive application of the methodology. Designers can perform requirements definition under this methodology as guidance towards the design of a successful PHM system, providing solutions for predicting remaining useful life (RUL) to support aircraft predictive maintenance.

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When an aircraft experiences an unexpected issue during flight operations, a technician determines whether the aircraft can safely perform the next flight. This operational decision process - known as dispatch assessment - has to happen within limited available time between aircraft arrival and departure. Currently, technicians face two main problems during the assessment: lack of access to decision support information and a time-consuming process for finding relevant information in extensive maintenance manuals. These issues often lead to delays and additional costs and are indicative of three larger challenges in the decision support domain: 1) a paucity of decision support models and applications for operational processes in maintenance; 2) relatively few efforts in applying and evaluating artifacts in experimental and real-life operational settings; and 3) a lack of systematic development, application and evaluation of digitization and automation efforts of complex decision processes in maintenance. This paper applies a design science research approach to address these challenges and introduces two novel artifacts: a decision support framework for real-time decision making in aircraft dispatch, and a web-based prototype tool accessible through mobile solutions. The practical relevance of the framework and prototype is validated through two representative application and evaluation studies, one in an experimental setting and one in an operational environment. Results show significant time savings and strong qualitative indications towards a higher incentive to use documentation and reducing human risk factors that lead to maintenance error.

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Remaining useful life (RUL) prediction is crucial for the implementation of Prognostics and Health Management (PHM) systems, enabling application of predictive maintenance strategies for critical systems (e.g. in aviation, power, railway). Existing literature addresses aspects of data-driven prognostic approaches, with a predominant focus on introducing and testing various novel prediction techniques which are purposed towards improving prediction accuracy performance. However, a relative lack of research can be identified when considering a comparative evaluation of competing for data-driven approaches. In particular, the contributing process elements and characteristics of data-driven prognostics methods are typically not compared in detail. To overcome these drawbacks, this paper aims to evaluate the underlying technical processes for statistical and artificial neural networks (ANN) methods for prognostics. A case study is conducted to implement both approaches on the PHM08 Challenge Data Set for comparison. This research comprehensively compares the statistical and ANN prognostic methods in a systematic manner, covering and comparing their respective technical processes, and evaluates the results with respect to prediction accuracy @en

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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Security risk management is essential for ensuring effective airport operations. This article introduces AbSRiM, a novel agent-based modeling and simulation approach to perform security risk management for airport operations that uses formal sociotechnical models that include temporal and spatial aspects. The approach contains four main steps: scope selection, agent-based model definition, risk assessment, and risk mitigation. The approach is based on traditional security risk management methodologies, but uses agent-based modeling and Monte Carlo simulation at its core. Agent-based modeling is used to model threat scenarios, and Monte Carlo simulations are then performed with this model to estimate security risks. The use of the AbSRiM approach is demonstrated with an illustrative case study. This case study includes a threat scenario in which an adversary attacks an airport terminal with an improvised explosive device. The approach provides a promising way to include important elements, such as human aspects and spatiotemporal aspects, in the assessment of risk. More research is still needed to better identify the strengths and weaknesses of the AbSRiM approach in different case studies, but results demonstrate the feasibility of the approach and its potential.@en

In this article, we present the development of a two-step optimization framework to deal with the design and selection of aircraft departure routes and the allocation of flights among these routes. The aim of the framework is to minimize cumulative noise annoyance and fuel burn. In the first step of the framework, multi-objective trajectory optimization is used to compute and store a set of routes that will serve as inputs in the second step. In the second step, the selection of routes from the sets of pre-computed optimal routes and the optimal allocation of flights to these routes are conducted simultaneously. To validate the proposed framework, we also conduct an analysis involving an integrated (one-step) approach, in which both trajectory optimization and route allocation are formulated as a single optimization problem. A comparison of both approaches is then performed, and their advantages and disadvantages are identified. The performance and capabilities of the present framework are demonstrated using a case study at Amsterdam Airport Schiphol in The Netherlands. The numerical results show that the proposed framework can generate solutions which can achieve a reduction in the number of people annoyed of up to 31% and a reduction in fuel consumption of 7.3% relative to the reference case solution.

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Analyzing agent-based models is a complex task. Agent-based models typically contain complex non-linear interactions between agents and generate emergent properties that cannot easily be explained. They are most commonly analyzed using sensitivity analysis techniques. While these techniques help understanding agent-based models better, they are not a one-size-fits-all solution. This paper explores the novel use of causal discovery algorithms from the field of causality as an additional means to analyze agent-based models. We propose the AbACaD methodology: Agent-based model Analysis using Causal Discovery. In this methodology, emergence in agent-based models is analyzed using causal discovery in combination with both machine learning and sensitivity analysis techniques. AbACaD combines different causal discovery algorithms, using a novel causal graph merging algorithm, to generate a causal graph based on agent-based simulation outcomes. This graph represents the causal relationships between the model parameters and the output variables of the model, and is then exploited to improve the understanding of emergent properties in the model. To demonstrate the effectiveness of AbACaD, it is applied to two models: the El Farol bar model, and an airport security and efficiency model. New emergent properties, such as the moment agents change their strategy in the El Farol bar model were identified. Furthermore, we found queue length to be an important factor in the number of casualties in an improvised explosive device (IED) attack. These emergent properties were well identified using AbACaD, but are hard to identify with traditional analysis techniques alone.

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This paper proposes an analytical model capable of relating damage found on a composite plate to a given impactor characteristic (size and energy). The model addresses a gap in knowledge regarding the types of damages to be expected over the lifetime of a new generation of composite aircraft. The damage type and dimensions are estimated using a superposition of local indentation and global plate deflection. The analytical approach, validated by drop-weight experiments, uniquely uses the impact characteristics predicted from metal aircraft damages as inputs to model the impact event response for composite plates under the same impact event conditions. The case study demonstrates that impact data from metal aircraft can be used to anticipate damage for a composite aircraft. The results from the model indicate that of the impactors that previously damaged metal aircraft, 75% will cause surface dent damage, fibre breakage, or penetration. As an extension of the analytical model application, a risk assessment is conducted on the predicted impactors, incorporating maintenance cost as the primary indicator for event consequences. This assessment shows the risks the similar events pose on metal vs. a comparable composite structure and allows aircraft operators to anticipate and plan maintenance actions.@en

AATOM, the Agent-based Airport Terminal Operations Model simulator is open-source, agent-based at its core, and contains several calibrated presets and templates of basic airport terminal components that can readily be used. Agents in this simulator follow the AATOM architecture, an activity-based architecture for human airport agents. This allows analysis based on agent activities, such as shopping and check-in, which is of vital interest for airports. The combination of agent-based modeling and the presence of basic airport terminal components makes AATOM a unique simulator, allowing the modeler to only focus on implementation of important features of their model. The usefulness of AATOM is demonstrated by presenting case studies in the areas of airport security, gate assignment and resilience.@en

The paper first investigates the influence of daily mobility of population on evaluation of aircraft noise effects. Then, a new air traffic assignment model that considers this activity is proposed. The main objective is to reduce the number of people affected by noise via lowering as much as possible the noise exposure level L _den of individuals or groups of people who commute to the same locations during the day. It is hereby intended to reduce the noise impact upon individuals rather than to reduce the impact in particular – typically densely populated – areas. However, sending aircraft farther away from populated regions to reduce noise impact may increase fuel burn, thus affecting airline costs and sustainability. Therefore, a multi-objective optimization approach is utilized to obtain reasonable solutions that comply with overall air transport sustainability. The method aims at generating a set of solutions that provide proper balance between noise annoyance and fuel consumption. The reliability and applicability of the proposed method are validated through a real case study at Belgrade airport in Serbia. The investigation shows that there is a difference between the number of people annoyed (NPA) evaluated based on the census data and the NPA evaluated based on the mobility data. In addition, these numbers differ significantly across residential locations. The optimal results show that the proposed model can offer a considerable reduction in the NPA, and in some cases, it can gain up to 77%, while maintaining the same level of fuel consumption compared with the reference case.

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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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Delays are costly to airlines in both money and image. A significant number of delays is caused by unexpected technical failures of aircraft systems or components. These failures, if not dealt with efficiently, can cause disruptions in the flight schedule and network. The annual costs of these type of disruptions add up to an estimated cost of € 2.8 billion in Europe alone. Determining the optimal course of action when an unexpected failure occurs is currently troublesome, leading to inefficient dispatch decision making.

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