QD
Q. Deng
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1
Modern aircraft have thousands of parts, systems, and components that need to be recurrently inspected or replaced. To keep the fleet airworthy, maintenance planners have to schedule the maintenance checks for each aircraft and the associated tasks. In practice, these two complex problems are solved following the experience of planners, resulting in sub-efficient solutions. This paper presents the first decision support system (DSS) developed for optimizing both aircraft maintenance check schedule and task allocation. The DSS integrates aircraft maintenance check scheduling, task allocation to each maintenance check, and shift planning in the same framework. The practical relevance of the DSS is illustrated through three test cases. The results show that the DSS can be used not only to optimize maintenance plans but also to study future maintenance policies. The results reveal substantial improvements in all key performance indicators compared with the planning approach followed by a partner airline.
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Modern aircraft have thousands of parts, systems, and components that need to be recurrently inspected or replaced. To keep the fleet airworthy, maintenance planners have to schedule the maintenance checks for each aircraft and the associated tasks. In practice, these two complex problems are solved following the experience of planners, resulting in sub-efficient solutions. This paper presents the first decision support system (DSS) developed for optimizing both aircraft maintenance check schedule and task allocation. The DSS integrates aircraft maintenance check scheduling, task allocation to each maintenance check, and shift planning in the same framework. The practical relevance of the DSS is illustrated through three test cases. The results show that the DSS can be used not only to optimize maintenance plans but also to study future maintenance policies. The results reveal substantial improvements in all key performance indicators compared with the planning approach followed by a partner airline.
This paper addresses the scheduling of aircraft maintenance tasks that must be carried out in multiple maintenance checks to keep a fleet of aircraft airworthy. The allocation of maintenance tasks to maintenance opportunities, also known as the task allocation problem (TAP), is a complex combinatorial problem that needs to be solved daily by maintenance operators. We propose a novel approach capable of efficiently solving the multi-year task allocation problem for a fleet of aircraft in a few minutes. We formulate this problem as a time-constrained variable-sized bin packing problem (TC-VS-BPP), extending the well-known variable-sized bin packing problem (VS-BPP) by adding deadlines, intervals, and arrivals for the repetition of tasks. In particular, we divide the planning horizon into variable size bins to which multidimensional tasks are allocated, subject to available labor power and task deadlines. To solve this problem, we propose a constructive heuristic based on the worst-fit decreasing (WFD) algorithm for TC-VS-BPP. The heuristic is tested and validated using the maintenance data of 45 aircraft from a European airline. Compared with the solution obtained with an approach using an exact method, the proposed heuristic is more than 30% faster for all the test cases discussed with the airline. Most of the cases have optimality gaps below 3%. Even for the extreme case, the optimality gap is still smaller than 5%.
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This paper addresses the scheduling of aircraft maintenance tasks that must be carried out in multiple maintenance checks to keep a fleet of aircraft airworthy. The allocation of maintenance tasks to maintenance opportunities, also known as the task allocation problem (TAP), is a complex combinatorial problem that needs to be solved daily by maintenance operators. We propose a novel approach capable of efficiently solving the multi-year task allocation problem for a fleet of aircraft in a few minutes. We formulate this problem as a time-constrained variable-sized bin packing problem (TC-VS-BPP), extending the well-known variable-sized bin packing problem (VS-BPP) by adding deadlines, intervals, and arrivals for the repetition of tasks. In particular, we divide the planning horizon into variable size bins to which multidimensional tasks are allocated, subject to available labor power and task deadlines. To solve this problem, we propose a constructive heuristic based on the worst-fit decreasing (WFD) algorithm for TC-VS-BPP. The heuristic is tested and validated using the maintenance data of 45 aircraft from a European airline. Compared with the solution obtained with an approach using an exact method, the proposed heuristic is more than 30% faster for all the test cases discussed with the airline. Most of the cases have optimality gaps below 3%. Even for the extreme case, the optimality gap is still smaller than 5%.
Long-term heavy maintenance check schedules are crucial in the aviation industry since airlines need them to prepare the required maintenance tools, workforce, and aircraft spare parts. However, most airlines adopt a manual approach to plan the heavy maintenance check schedules in current practice. This manual process relies on the experience of their maintenance planners, and the resulting heavy maintenance schedules need frequent adjustment because of uncertainty. This paper applies a genetic algorithm (GA) to generate robust aircraft heavy maintenance check schedules. It aims to reduce the workload and the frequency of revising heavy maintenance schedules considering uncertainties associated with heavy maintenance check duration and aircraft daily utilization. A major European airline case study shows that the GA finds robust and efficient multi-year aircraft heavy maintenance schedules for a fleet of 45 aircraft in 30 minutes. Compared with the current approach followed by the airline, the algorithm reduces the total number of heavy maintenance checks by 7% while increasing utilization by 4.4%, which could potentially lead to a reduction of direct annual maintenance costs between $122.5K and $612.5K. Furthermore, when testing the robustness of the 4-years maintenance check schedules produced, a Monte Carlo analysis has shown that all aircraft could be maintained before their check due date for 41% of the episodes simulated, compared to 0.27% of the episodes for the single deterministic scenario approach.
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Long-term heavy maintenance check schedules are crucial in the aviation industry since airlines need them to prepare the required maintenance tools, workforce, and aircraft spare parts. However, most airlines adopt a manual approach to plan the heavy maintenance check schedules in current practice. This manual process relies on the experience of their maintenance planners, and the resulting heavy maintenance schedules need frequent adjustment because of uncertainty. This paper applies a genetic algorithm (GA) to generate robust aircraft heavy maintenance check schedules. It aims to reduce the workload and the frequency of revising heavy maintenance schedules considering uncertainties associated with heavy maintenance check duration and aircraft daily utilization. A major European airline case study shows that the GA finds robust and efficient multi-year aircraft heavy maintenance schedules for a fleet of 45 aircraft in 30 minutes. Compared with the current approach followed by the airline, the algorithm reduces the total number of heavy maintenance checks by 7% while increasing utilization by 4.4%, which could potentially lead to a reduction of direct annual maintenance costs between $122.5K and $612.5K. Furthermore, when testing the robustness of the 4-years maintenance check schedules produced, a Monte Carlo analysis has shown that all aircraft could be maintained before their check due date for 41% of the episodes simulated, compared to 0.27% of the episodes for the single deterministic scenario approach.
This paper proposes a lookahead approximate dynamic programming methodology for aircraft maintenance check scheduling, considering the uncertainty of aircraft daily utilization and maintenance check elapsed time. It adopts a dynamic programming framework, using a hybrid lookahead scheduling policy. The hybrid lookahead scheduling policy makes the one-step optimal decision for heavy aircraft maintenance based on deterministic forecasts and then determines the light maintenance according to stochastic forecasts. The objective is to minimize the total wasted utilization interval between maintenance checks while reducing the need for additional maintenance slots. By achieving this goal, one is also reducing the number of maintenance checks and increasing aircraft availability while respecting airworthiness regulations. We validate the proposed methodology using the fleet maintenance data from a major European airline. The descriptive statistics of several test runs show that, when compared with the current practice, the proposed methodology potentially reduces the number of A-checks by 1.9%, the number of C-checks by 9.8%, and the number of additional slots by 78.3% over four years.
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This paper proposes a lookahead approximate dynamic programming methodology for aircraft maintenance check scheduling, considering the uncertainty of aircraft daily utilization and maintenance check elapsed time. It adopts a dynamic programming framework, using a hybrid lookahead scheduling policy. The hybrid lookahead scheduling policy makes the one-step optimal decision for heavy aircraft maintenance based on deterministic forecasts and then determines the light maintenance according to stochastic forecasts. The objective is to minimize the total wasted utilization interval between maintenance checks while reducing the need for additional maintenance slots. By achieving this goal, one is also reducing the number of maintenance checks and increasing aircraft availability while respecting airworthiness regulations. We validate the proposed methodology using the fleet maintenance data from a major European airline. The descriptive statistics of several test runs show that, when compared with the current practice, the proposed methodology potentially reduces the number of A-checks by 1.9%, the number of C-checks by 9.8%, and the number of additional slots by 78.3% over four years.
Optimal Decision Making for Aircraft Maintenance Planning
From Maintenance Check Scheduling to Maintenance Task Allocation
Aircraft maintenance is the process of overhaul, repair, inspection, or modification of an aircraft or aircraft systems, components, and structures, to keep these in an airworthy condition. Airlines must perform regular maintenance on their fleet to keep their aircraft airworthy and, ultimately, prevent any systems or components failures during commercial operations. Coupled with the rapid growth of the global commercial aircraft fleet, aircraft maintenance demands have increased significantly in the past few decades. Since aviation is a very competitive industry, the growing aircraft maintenance demands and associated operation costs put a huge financial burden on airlines, forcing them to reduce costs while still respecting safety regulations. Therefore, airlines are laying increasing emphasis on planning aircraft maintenance efficiently. An efficient planning approach for aircraft maintenance is a dual-edged sword. It reduces not only the time and effort of organizing maintenance tasks and coordinating maintenance activities but also increases the time fleet availability for operations and associated revenues. Before introducing wide-body aircraft in the 1970s, airlines used a bottom-up, task-oriented approach to plan aircraft maintenance, as then the commercial fleet sizeswere small. Nowadays, most airlines adopt a top-down approach, and first groups the maintenance tasks with the same or similar inspection intervals into a large task block. These, in turn, are commonly divided into four types and labeled as: A-check (every 4–6 months), B-check (every 4–6 months), C-check (every 18–24 months), and Dcheck (every 6–10 years). After planning the letter checks, airlines further determine the maintenance tasks to be added or removed in each letter check. This dissertation innovates the aircraft maintenance planning (AMP) process by presenting a comprehensive digital solution. It replaces the current sequential computeraided manual approachwith an integrated scheduling methodology to automate the aircraft maintenance planning process. Given a specific time horizon, it considers all check types together when making the maintenance check decisions and generates the optimal schedules for all letter checks in one comprehensive solution. After that, it plans a long-term (3–5 years) task execution plan based on the optimal maintenance check schedule. These features are integrated into a decision supp
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Aircraft maintenance is the process of overhaul, repair, inspection, or modification of an aircraft or aircraft systems, components, and structures, to keep these in an airworthy condition. Airlines must perform regular maintenance on their fleet to keep their aircraft airworthy and, ultimately, prevent any systems or components failures during commercial operations. Coupled with the rapid growth of the global commercial aircraft fleet, aircraft maintenance demands have increased significantly in the past few decades. Since aviation is a very competitive industry, the growing aircraft maintenance demands and associated operation costs put a huge financial burden on airlines, forcing them to reduce costs while still respecting safety regulations. Therefore, airlines are laying increasing emphasis on planning aircraft maintenance efficiently. An efficient planning approach for aircraft maintenance is a dual-edged sword. It reduces not only the time and effort of organizing maintenance tasks and coordinating maintenance activities but also increases the time fleet availability for operations and associated revenues. Before introducing wide-body aircraft in the 1970s, airlines used a bottom-up, task-oriented approach to plan aircraft maintenance, as then the commercial fleet sizeswere small. Nowadays, most airlines adopt a top-down approach, and first groups the maintenance tasks with the same or similar inspection intervals into a large task block. These, in turn, are commonly divided into four types and labeled as: A-check (every 4–6 months), B-check (every 4–6 months), C-check (every 18–24 months), and Dcheck (every 6–10 years). After planning the letter checks, airlines further determine the maintenance tasks to be added or removed in each letter check. This dissertation innovates the aircraft maintenance planning (AMP) process by presenting a comprehensive digital solution. It replaces the current sequential computeraided manual approachwith an integrated scheduling methodology to automate the aircraft maintenance planning process. Given a specific time horizon, it considers all check types together when making the maintenance check decisions and generates the optimal schedules for all letter checks in one comprehensive solution. After that, it plans a long-term (3–5 years) task execution plan based on the optimal maintenance check schedule. These features are integrated into a decision supp
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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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.
In this paper we evaluate the performance of platoon enabled by contemporary ITS-G5 vehicular communications through the number of simulation experiments. We assess platooning fuel consumption performance under two communication setups and estimate the potential influence of the communication system on the efficiency of the platooning. We also make an attempt to transform our results on platoon fuel efficiency into potential cost reduction gain. Our study shows that platooning fuel-efficiency may vary depending on the communication setup.
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In this paper we evaluate the performance of platoon enabled by contemporary ITS-G5 vehicular communications through the number of simulation experiments. We assess platooning fuel consumption performance under two communication setups and estimate the potential influence of the communication system on the efficiency of the platooning. We also make an attempt to transform our results on platoon fuel efficiency into potential cost reduction gain. Our study shows that platooning fuel-efficiency may vary depending on the communication setup.
Platooning heavy-duty vehicles (HDVs) on a highway is a method for improving energy and transport efficiency. On one hand, HDV platoon driving in small intervehicle distances could increase highway capacity; on the other hand, HDVs traveling in small intervehicle distances experience significant air-drag reduction and, therefore, improve fuel efficiency. However, although the majority of research has been conducted on the development of platoon systems, very few studies have focused on quantification of the impacts of HDV platooning on traffic flow. This paper initializes a simulation framework to facilitate the study of HDV platooning and establishes the corresponding concept and operations. The longitudinal driving behaviors of HDV platoons are modeled in detail, considering the acceleration capability of an HDV. The proposed framework is applied on three experimental cases: the first case is to study the impacts of HDV platooning on traffic flow and the second and third cases are about the influence of traffic on HDV platoon formation. In the first case, simulation outcomes show that the increasing percentage of HDV platooning in traffic flow generally results in more dramatic improvements on traffic efficiency, while preserving traffic safety for passenger vehicles. In the second and third cases, for the HDV platoon formation, deceleration of the first HDV to a low speed during platoon formation will increase the formation time to a large extent in medium and heavy traffic.
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Platooning heavy-duty vehicles (HDVs) on a highway is a method for improving energy and transport efficiency. On one hand, HDV platoon driving in small intervehicle distances could increase highway capacity; on the other hand, HDVs traveling in small intervehicle distances experience significant air-drag reduction and, therefore, improve fuel efficiency. However, although the majority of research has been conducted on the development of platoon systems, very few studies have focused on quantification of the impacts of HDV platooning on traffic flow. This paper initializes a simulation framework to facilitate the study of HDV platooning and establishes the corresponding concept and operations. The longitudinal driving behaviors of HDV platoons are modeled in detail, considering the acceleration capability of an HDV. The proposed framework is applied on three experimental cases: the first case is to study the impacts of HDV platooning on traffic flow and the second and third cases are about the influence of traffic on HDV platoon formation. In the first case, simulation outcomes show that the increasing percentage of HDV platooning in traffic flow generally results in more dramatic improvements on traffic efficiency, while preserving traffic safety for passenger vehicles. In the second and third cases, for the HDV platoon formation, deceleration of the first HDV to a low speed during platoon formation will increase the formation time to a large extent in medium and heavy traffic.