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I. Tseremoglou
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Aircraft maintenance scheduling is a focus point for airlines. Maintenance is essential to ensure the airworthiness of aircraft, but it comes at the cost of rendering them unavailable for operations. In current operations, aircraft maintenance scheduling must often be updated to include time for non-routine and non-schedule tasks. These non-routine tasks can increase costs, maintenance workload, and uncertainty of the airlines’ operations. This research introduces a supervised learning framework designed to forecast future non-routine task workloads accurately, improving the accuracy of the planned maintenance schedule. This framework consists of two random forest predictors which estimate the amount of non-routine tasks and the number of future work hours that should be allocated in advance for potential non-routine tasks. Our approach produces highly reliable predictions by leveraging a robust dataset obtained from an international airline. The results show an average of 20% improvement versus an existing on-site sampling method. Furthermore, our in-depth analysis of prediction distributions enables the identification of the underlying causes of significant prediction errors, shedding light on the unpredictabilities inherent to non-routine tasks.
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Aircraft maintenance scheduling is a focus point for airlines. Maintenance is essential to ensure the airworthiness of aircraft, but it comes at the cost of rendering them unavailable for operations. In current operations, aircraft maintenance scheduling must often be updated to include time for non-routine and non-schedule tasks. These non-routine tasks can increase costs, maintenance workload, and uncertainty of the airlines’ operations. This research introduces a supervised learning framework designed to forecast future non-routine task workloads accurately, improving the accuracy of the planned maintenance schedule. This framework consists of two random forest predictors which estimate the amount of non-routine tasks and the number of future work hours that should be allocated in advance for potential non-routine tasks. Our approach produces highly reliable predictions by leveraging a robust dataset obtained from an international airline. The results show an average of 20% improvement versus an existing on-site sampling method. Furthermore, our in-depth analysis of prediction distributions enables the identification of the underlying causes of significant prediction errors, shedding light on the unpredictabilities inherent to non-routine tasks.
Condition-Based Maintenance scheduling of an aircraft fleet under partial observability
A Deep Reinforcement Learning approach
In the Condition-Based Maintenance (CBM) context, the definition of optimal maintenance plans for an aircraft fleet depends on an efficient integration of : (i) the probabilistic predictions of the health condition of the components and (ii) the stochastic arrival of the corrective maintenance tasks, together with consideration of the preventive maintenance tasks as defined in the Maintenance Planning Document (MPD). To this end, in this paper, we present a two-stage dynamic scheduling framework to solve the aircraft fleet maintenance scheduling problem under a CBM strategy in a disruptive environment. In the first stage of the framework, we address the uncertainty in the predicted health state of the monitored components by planning the optimal maintenance policy based upon the belief state-space of the health of the components. The decision-making process is formulated as a Partially Observable Markov Decision Process (POMDP) and is solved using the Partially Observable Monte Carlo Planning (POMCP) algorithm, considering the aircraft maintenance scheduling problem requirements. In the second stage, a Deep Q-Network (DQN) is developed, that integrates the defined maintenance policy of the monitored components within the scheduling of the aircraft fleet's preventive and corrective maintenance tasks. Our model, through a rolling horizon approach, continuously creates and adjusts the maintenance schedule, reacting to new updated task information, where the availability of maintenance resources constraints the execution of each task. The proposed framework was tested on a case study from a large airline and the performance was evaluated against the current state practice of the airline. The results show that our model can schedule 96.4% of monitored components on-time. As a consequence of this, a 46.2% maintenance cost reduction is achieved for the considered monitored components relative to a corrective maintenance approach.
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In the Condition-Based Maintenance (CBM) context, the definition of optimal maintenance plans for an aircraft fleet depends on an efficient integration of : (i) the probabilistic predictions of the health condition of the components and (ii) the stochastic arrival of the corrective maintenance tasks, together with consideration of the preventive maintenance tasks as defined in the Maintenance Planning Document (MPD). To this end, in this paper, we present a two-stage dynamic scheduling framework to solve the aircraft fleet maintenance scheduling problem under a CBM strategy in a disruptive environment. In the first stage of the framework, we address the uncertainty in the predicted health state of the monitored components by planning the optimal maintenance policy based upon the belief state-space of the health of the components. The decision-making process is formulated as a Partially Observable Markov Decision Process (POMDP) and is solved using the Partially Observable Monte Carlo Planning (POMCP) algorithm, considering the aircraft maintenance scheduling problem requirements. In the second stage, a Deep Q-Network (DQN) is developed, that integrates the defined maintenance policy of the monitored components within the scheduling of the aircraft fleet's preventive and corrective maintenance tasks. Our model, through a rolling horizon approach, continuously creates and adjusts the maintenance schedule, reacting to new updated task information, where the availability of maintenance resources constraints the execution of each task. The proposed framework was tested on a case study from a large airline and the performance was evaluated against the current state practice of the airline. The results show that our model can schedule 96.4% of monitored components on-time. As a consequence of this, a 46.2% maintenance cost reduction is achieved for the considered monitored components relative to a corrective maintenance approach.
Despite its success in various research domains, Reinforcement Learning (RL) faces challenges in its application to air transport operations due to the rigorous certification standards of the aviation industry. The existing regulatory framework fails to provide adequate, acceptable means of compliance for RL applications, and thus, there is no legal framework for their safe deployment yet. Guidelines must be formulated to certify RL models aimed at air transport operations to enable real-world utilisation of these promising methods. These guidelines must consider the unique characteristics of these models, deviating from the methodology of current guidelines crafted before the emergence of ML applications. The paper proposes novel certification requirements for RL models based on their technical characteristics, safety-criticality, and autonomy. This framework covers the choice of the RL algorithm and analyses the actions, agents, environment, and potential hazards and risks of the RL application. Additionally, this work outlines the evidence the certification applicant must present to demonstrate compliance with these requirements. While this framework is not a complete solution for the complex problem of certifying RL, it is intended to serve as an initial framework which can be extended in cooperation with regulatory entities.
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Despite its success in various research domains, Reinforcement Learning (RL) faces challenges in its application to air transport operations due to the rigorous certification standards of the aviation industry. The existing regulatory framework fails to provide adequate, acceptable means of compliance for RL applications, and thus, there is no legal framework for their safe deployment yet. Guidelines must be formulated to certify RL models aimed at air transport operations to enable real-world utilisation of these promising methods. These guidelines must consider the unique characteristics of these models, deviating from the methodology of current guidelines crafted before the emergence of ML applications. The paper proposes novel certification requirements for RL models based on their technical characteristics, safety-criticality, and autonomy. This framework covers the choice of the RL algorithm and analyses the actions, agents, environment, and potential hazards and risks of the RL application. Additionally, this work outlines the evidence the certification applicant must present to demonstrate compliance with these requirements. While this framework is not a complete solution for the complex problem of certifying RL, it is intended to serve as an initial framework which can be extended in cooperation with regulatory entities.
Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncertainty included in health prognostics. This type of problem falls under the broad category of resource-constrained scheduling problems under uncertainty and is often solved using a mixed integer linear programming (MILP) formulation. While a MILP framework is very promising, the problem size can scale exponentially with the number of considered aircraft and considered tasks, leading to significantly high computational costs. The most recent advances in artificial intelligence have demonstrated the capability of deep reinforcement learning (DRL) algorithms to alleviate this curse of dimensionality, as once the DRL agent is trained, it can achieve real-time optimization of the maintenance schedule. However, there is no guarantee of optimality. These comparative merits of a MILP and a DRL formulation for the aircraft fleet maintenance scheduling problem have not been discussed in the literature. This study is a response to this research gap. We conduct a comparison of a MILP and a DRL scheduling model, which are used to derive the optimal maintenance schedule for various maintenance scenarios for aircraft fleets of different sizes in a disruptive environment, while considering health prognostics and the available resources for the execution of each task. The quality of solutions is evaluated on the basis of four planning objectives, defined according to real airline practice. The results show that the DRL approach achieves better results with respect to scheduling of prognostics-driven tasks and requires less computational time, whereas the MILP model produces more stable maintenance schedules and induces less maintenance ground time. Overall, the comparison provides valuable insights for the integration of health prognostics in airline maintenance practice.
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Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncertainty included in health prognostics. This type of problem falls under the broad category of resource-constrained scheduling problems under uncertainty and is often solved using a mixed integer linear programming (MILP) formulation. While a MILP framework is very promising, the problem size can scale exponentially with the number of considered aircraft and considered tasks, leading to significantly high computational costs. The most recent advances in artificial intelligence have demonstrated the capability of deep reinforcement learning (DRL) algorithms to alleviate this curse of dimensionality, as once the DRL agent is trained, it can achieve real-time optimization of the maintenance schedule. However, there is no guarantee of optimality. These comparative merits of a MILP and a DRL formulation for the aircraft fleet maintenance scheduling problem have not been discussed in the literature. This study is a response to this research gap. We conduct a comparison of a MILP and a DRL scheduling model, which are used to derive the optimal maintenance schedule for various maintenance scenarios for aircraft fleets of different sizes in a disruptive environment, while considering health prognostics and the available resources for the execution of each task. The quality of solutions is evaluated on the basis of four planning objectives, defined according to real airline practice. The results show that the DRL approach achieves better results with respect to scheduling of prognostics-driven tasks and requires less computational time, whereas the MILP model produces more stable maintenance schedules and induces less maintenance ground time. Overall, the comparison provides valuable insights for the integration of health prognostics in airline maintenance practice.
In this paper, we present a combined forecasting and optimization decision-support tool to assist air cargo revenue management departments in the acceptance/rejection process of incoming cargo bookings. We consider the case of a combination airline and focus on the passenger aircraft belly capacity. The process is dynamic (bookings are received in a discrete fashion during the booking horizon) and uncertain (for some bookings the three dimensions are not provided, while the actual belly space available for cargo is only revealed a few hours before departure). Hence, analysts base decisions on historical data or human experience, which might yield sub-optimal or infeasible solutions due to the aforementioned uncertainties. We tackle them by proposing data-driven algorithms to predict available cargo space and shipment dimensions. A packing problem is solved sequentially once a new booking request is received, predicting shipment dimensions, if necessary, and considering the uncertainty of such prediction. The booking is accepted if it results in a feasible loading configuration where no previously accepted booking is offloaded. When applied in a deterministic context, our packing method outperformed the one used by the partner airline, increasing the loaded volume up to 20%. The framework was also tested assuming unknown shipment dimensions, comparing a risk-prone and a risk-averse strategy, with the latter accounting for uncertainty in dimension predictions and the former using mean values. While the average loaded volume decreases in the risk-averse case, the number of unplanned offloadings due to under-predicted dimensions decreases from 54% to 12% of the simulated cases, hence yielding a more robust acceptance strategy.
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In this paper, we present a combined forecasting and optimization decision-support tool to assist air cargo revenue management departments in the acceptance/rejection process of incoming cargo bookings. We consider the case of a combination airline and focus on the passenger aircraft belly capacity. The process is dynamic (bookings are received in a discrete fashion during the booking horizon) and uncertain (for some bookings the three dimensions are not provided, while the actual belly space available for cargo is only revealed a few hours before departure). Hence, analysts base decisions on historical data or human experience, which might yield sub-optimal or infeasible solutions due to the aforementioned uncertainties. We tackle them by proposing data-driven algorithms to predict available cargo space and shipment dimensions. A packing problem is solved sequentially once a new booking request is received, predicting shipment dimensions, if necessary, and considering the uncertainty of such prediction. The booking is accepted if it results in a feasible loading configuration where no previously accepted booking is offloaded. When applied in a deterministic context, our packing method outperformed the one used by the partner airline, increasing the loaded volume up to 20%. The framework was also tested assuming unknown shipment dimensions, comparing a risk-prone and a risk-averse strategy, with the latter accounting for uncertainty in dimension predictions and the former using mean values. While the average loaded volume decreases in the risk-averse case, the number of unplanned offloadings due to under-predicted dimensions decreases from 54% to 12% of the simulated cases, hence yielding a more robust acceptance strategy.
Conference paper
(2022)
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I. Tseremoglou, M.T. Bieber, W.J.C. Verhagen, Bruno F. Santos, F.C. Freeman, P.J. van Kessel
One of the challenges of Condition-Based Maintenance (CBM) is to combine health monitoring and predictions with efficient scheduling tools. However, the majority of literature is focusing on the assessment of prognostics algorithms performance. In fact, the added value of these algorithms can only be assessed when considering their impact on maintenance decision process. Furthermore, in practice, when considering the scenario of an aircraft fleet with multiple monitored components, it is hard for a human decision-maker to translate and identify the effect of probabilistic results from all prognostics models from all systems on the maintenance schedule. Therefore, to support the implementation of CBM, the prognostics algorithms have to be integrated within a scheduling framework. Our paper proposes this integration in order to evaluate the impact of different level of prognostics accuracy and uncertainty on the aircraft fleet maintenance scheduling level. First, a Support Vector Regression (SVR) model is used to predict the Remaining Useful Life (RUL) distributions of the monitored components. Second, the maintenance scheduling problem is solved within a Reinforcement Learning (RL) approach incorporating a state-of-the-art Partially Observable Monte Carlo algorithm. Implementing a rolling horizon approach, our proposed framework is applied to a fleet of 10 aircraft, each equipped with multiple monitored systems. A case study with multiple different prediction accuracy and uncertainty scenarios is performed to assess the impact of prognostics uncertainty on optimal maintenance scheduling. The performed analysis aims to guide the development and assessment of prognostic models in terms of accuracy and uncertainty in the context of CBM.
...
One of the challenges of Condition-Based Maintenance (CBM) is to combine health monitoring and predictions with efficient scheduling tools. However, the majority of literature is focusing on the assessment of prognostics algorithms performance. In fact, the added value of these algorithms can only be assessed when considering their impact on maintenance decision process. Furthermore, in practice, when considering the scenario of an aircraft fleet with multiple monitored components, it is hard for a human decision-maker to translate and identify the effect of probabilistic results from all prognostics models from all systems on the maintenance schedule. Therefore, to support the implementation of CBM, the prognostics algorithms have to be integrated within a scheduling framework. Our paper proposes this integration in order to evaluate the impact of different level of prognostics accuracy and uncertainty on the aircraft fleet maintenance scheduling level. First, a Support Vector Regression (SVR) model is used to predict the Remaining Useful Life (RUL) distributions of the monitored components. Second, the maintenance scheduling problem is solved within a Reinforcement Learning (RL) approach incorporating a state-of-the-art Partially Observable Monte Carlo algorithm. Implementing a rolling horizon approach, our proposed framework is applied to a fleet of 10 aircraft, each equipped with multiple monitored systems. A case study with multiple different prediction accuracy and uncertainty scenarios is performed to assess the impact of prognostics uncertainty on optimal maintenance scheduling. The performed analysis aims to guide the development and assessment of prognostic models in terms of accuracy and uncertainty in the context of CBM.