Heuristics for flight and maintenance planning of mission aircraft (original) (raw)
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Heuristics for maximizing fleet availability subject to flight & maintenance requirements
2008
Flight and Maintenance Planning (FMP) addresses the question of which available aircraft should fly and for how long, and which grounded aircraft should perform maintenance operations, in a group of aircraft that comprise a unit. The objective is to achieve maximum availability of the unit over the planning horizon. In this work, we develop three heuristic solution procedures for the FMP problem. We present computational results which illustrate the computational performance of these procedures and evaluate the quality of the solutions that they produce. These results are very satisfactory, because they demonstrate that, under careful consideration, even large FMP instances can be handled quite effectively.
European Journal of Operational Research, 2015
We address the Flight and Maintenance Planning (FMP) problem, i.e., the problem of deciding which available aircraft to fly and for how long, and which grounded aircraft to perform maintenance operations on in a group of aircraft that comprise a unit. The aim is to maximize the unit fleet availability over a multiperiod planning horizon, while also ensuring that certain flight and maintenance requirements are satisfied. Heuristic approaches that are used in practice to solve the FMP problem often perform poorly, generating solutions that are far from the optimum. On the other hand, the exact optimization models that have been developed to tackle the problem handle small problems effectively, but tend to be computationally inefficient for larger problems, such as the ones that arise in practice. With these in mind, we develop an exact solution algorithm for the FMP problem, which is capable of identifying the optimal solution of considerably large realistic problems in reasonable computational times. The algorithm solves suitable relaxations of the original problem, utilizing valid cuts that guide the search towards the optimal solution. We present extensive experimental results, which demonstrate that the algorithm's performance on realistic problems is superior to that of two popular commercial optimization software packages, whereas the opposite is true for a class of problems with special characteristics that deviate considerably from those of realistic problems. The important conclusion of this research is that the proposed algorithm, complemented by generic optimization software, can handle effectively a large variety of FMP problem instances.
Flight and Maintenance Planning of Military Aircraft for Maximum Fleet Availability
2010
Every aircraft, military or civilian, must be grounded for maintenance after it has completed a certain number of flight hours since its last maintenance check. Flight and maintenance planning of military aircraft addresses the problem of deciding which available aircraft to fly and for how long, and which grounded aircraft to perform maintenance operations on, in a set of aircraft that comprise a combat unit. The objective is to achieve maximum availability of the unit over the planning horizon. In this work, we develop a biobjective optimization model of the flight and maintenance planning problem, and we illustrate its application and solution on a real life instance drawn from the Hellenic Air Force. We formulate the flight and maintenance planning problem as a mixed integer linear program, with two objectives: total number of available aircraft and total residual flight time. The residual flight time of an available aircraft is defined as the total remaining time that this aircraft can fly, until it has to be grounded for maintenance check. For the solution of the problem we apply the weighted sums approach and lexicographic optimization. By comparing and analyzing the solutions obtained, we get insight into the behavior of the model. We conclude with a discussion based on these results and suggestions on how the model can be extended in the future.
Computers & Industrial Engineering, 2017
We consider the FMP problem encountered in the Hellenic Air Force (HAF), that is, the problem of issuing individual flight and maintenance plans for a group of aircraft comprising a unit, so as to maximize the fleet availability of the unit over a multi-period planning horizon while also satisfying various flight and maintenance related restrictions. The optimization models that have been developed to tackle this problem often perform unsatisfactorily, providing solutions for which the fleet availability exhibits significant variability. In order to handle this difficulty, in this work we develop a mixed integer programming model, which, besides the typical objective maximizing the fleet availability, also includes an additional objective that minimizes its variability. Motivated by the substantial computational difficulties the typical ε-constraint reduced feasible region approach is faced with, as a result of the solution complexity of the optimization models involved, we also develop two specialized solution methodologies for this problem. Both methodologies identify the entire frontier of non-dominated solutions, utilizing suitable relaxations of the original model and exploiting the fact that the domain comprising possible fleet availability values is a discrete set. The first one disaggregates the original FMP model into smaller subproblems whose solution is attained much more efficiently. The second one is a variant of the ε-constraint method, applied to a suitable relaxation of the original FMP model. We present extensive computational results assessing the efficiency of the proposed solution methodologies and demonstrating that their performance is significantly superior to that of the typical ε-constraint method applied directly to the original biobjective model.
Mixed integer least squares optimization for flight and maintenance planning of mission aircraft
Naval Research Logistics (NRL), 2012
ABSTRACT We address the problem of generating a joint flight and maintenance plan for a unit of mission aircraft. The objective is to establish a balanced allocation of the flight load and the maintenance capacity to the individual aircraft of the unit, so that its long-term availability is kept at a high and steady level. We propose a mixed integer nonlinear model to formulate the problem, the objective function of which minimizes a least squares index expressing the total deviation of the individual aircraft flight and maintenance times from their corresponding target values. Using the model's special structure and properties, we develop an exact search algorithm for its solution. We analyze the computational complexity of this algorithm, and we present computational results comparing its performance against that of a commercial optimization package. Besides demonstrating the superiority of the proposed algorithm, these results reveal that the total computational effort required for the solution of the problem depends mainly on two crucial parameters: the size of the unit (i.e., the number of aircraft that comprise it) and the space capacity of the maintenance station. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012
Operational aircraft maintenance routing problem with remaining time consideration
European Journal of Operational Research, 2014
The aircraft maintenance routing problem is one of the most studied problems in the airline industry. Most of the studies focus on finding a unique rotation that will be repeated by each aircraft in the fleet with a certain lag. In practice, using a single rotation for the entire fleet is not applicable due to stochasticity and operational considerations in the airline industry. In this study, our aim is to develop a fast responsive methodology which provides maintenance feasible routes for each aircraft in the fleet over a weekly planning horizon with the objective of maximizing utilization of the total remaining flying time of fleet. For this purpose, we formulate an integer linear programming (ILP) model by modifying the connection network representation. The proposed model is solved by using branch-and-bound under different priority settings for variables to branch on. A heuristic method based on compressed annealing is applied to the same problem and a comparison of exact and heuristic methods are provided. The model and the heuristic method are extended to incorporate maintenance capacity constraints. Additionally, a rolling horizon based procedure is proposed to update the existing routes when some of the maintenance decisions are already fixed.
Systems analysis for planning of air fleets and maintenance facilities
1979
The high cost and extraordinary demands made on sophisticated air defence systems, pose hard challenges to the managers and engineers who plan the operation and maintenance of such systems. This paper presents a study aimed at developing simulation and systems analysis techniques for the effective planning and efficient operation of small fleets of aircraft, typical of the air force of a developing country. We consider an important aspect of fleet management: the problem of resource allocation for achieving prescribed operational effectiveness of the fleet. At this stage, we consider a single flying-base, where the operationally ready aircraft are stationed, and a repair-depot, where the planes are overhauled. An important measure of operational effectiveness is' availability ', which may be defined as the expected fraction of the fleet fit for use at a given instant. The tour of aircraft in a flying-base, repair-depot system through a cycle of' operationally ready' and' scheduled overhaul' phases is represented first by a deterministic flow process and then by a cyclic queuing process. Initially the steady-state availability at the flying-base is computed under the assumptions of Poisson arrivals, exponential service times and an equivalent singleserver repair-depot. This analysis also brings out the effect of fleet size on availability. It defines a ' small ' fleet essentially in terms of the important ' traffic' parameter of service rate/maximum arrival rate. A simulation model of the system has been developed using GPSS to study sensitivity to distributional assumptions, to validate the principal assumptions of the analytical model such as the single-server assumption and to obtain confidence intervals for the statistical parameters of interest.
An optimisation framework for airline fleet maintenance scheduling and tail assignment
2019
Fierce competition between airlines has led to the need of minimising the operating costs while also ensuring quality of service. Given the large proportion of operating costs dedicated to aircraft maintenance, cooperation between airlines and their respective maintenance provider is paramount. In this research, we propose a framework to develop commercially viable and maintenance feasible flight and maintenance schedules. Such framework involves two multi-objective mixed integer linear programming (MMILP) formulations and an iterative algorithm. The first formulation, the airline fleet maintenance scheduling (AMS) with violations, minimises the number of maintenance regulation violations and the number of not airworthy aircraft; subject to limited workshop resources and current maintenance regulations on individual aircraft flying hours. The second formulation, the AMS with tail assignment (TA) allows aircraft to be assigned to different flights. In this case, subject to similar constraints as the first formulation, six lexicographically ordered objective functions are minimised. Namely, the number of violations, maximum resource level, number of tail reassignments, number of maintenance interventions, overall resource usage, and the amount of maintenance required by each aircraft at the end of the planning horizon. The iterative algorithm ensures fast computational times while providing good quality solutions. Additionally, by tracking aircraft and using precise flying hours between maintenance opportunities, we ensure that the aircraft are airworthy at all times. Computational tests on real flight schedules over a 30-day planning horizon show that even with multiple airlines and workshops (160 0 0 flights, 529 aircraft, 8 maintenance workshops), our solution approach can construct near-optimal maintenance schedules within minutes.
Fleet-Level Selective Maintenance and Aircraft Scheduling
The objective of this research is to investigate the use of a mathematical modeling methodology for integrating maintenance planning and sortie scheduling issues. First, the relevant research literature for both selective maintenance and fleet assignment is presented. Next, background research is presented, which extends a current selective maintenance model to incorporate sets of systems. Here a selective maintenance model for a set of systems that must execute a set of missions with system maintenance performed only between missions is defined. Finally, we formulate a more complex optimization model that addresses a more dynamic mission profile. Specifically, missions start and end at different times, and maintenance and scheduling decisions are made over a series of time "buckets." We consider a planning horizon such that each system in the set returns from its previous mission and begins its future mission; however, no system returns from its future before the end of t...
Transportation Research Part B: Methodological, 2020
Fierce competition between airlines has led to the need of minimising the operating costs while also ensuring quality of service. Given the large proportion of operating costs dedicated to aircraft maintenance, cooperation between airlines and their respective maintenance provider is paramount. In this research, we propose a framework to develop commercially viable and maintenance feasible flight and maintenance schedules. Such framework involves two multiobjective mixed integer linear programming (MMILP) formulations and an iterative algorithm. The first formulation, the airline fleet maintenance scheduling (AMS) with violations, minimises the number of maintenance regulation violations and the number of not airworthy aircraft; subject to limited workshop resources and current maintenance regulations on individual aircraft flying hours. The second formulation, the AMS with tail assignment (TA) allows aircraft to be assigned to different flights. In this case, subject to similar constraints as the first formulation, six lexicographically ordered objective functions are minimised. Namely, the number of violations, maximum resource level, number of tail re-assignments, number of maintenance interventions, overall resource usage, and number of not airworthy aircraft. The iterative algorithm ensures fast computational times while providing good quality solutions. Additionally, by tracking aircraft and using precise flying hours between maintenance opportunities we ensure that the aircraft are airworthy at all times. Computational tests on real flight schedules over a 30-day planning horizon show that even with multiple airlines (6457 flights, 1032 aircraft, 5 maintenance workshops) our solution approach can construct near optimal maintenance schedules within minutes.