A multiobjective model for maximizing fleet availability under the presence of flight and maintenance requirements

Kozanidis, George

doi:10.1002/atr.5670430205

Cited by 21 publications

(17 citation statements)

References 9 publications

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“…The overall value intervals of the non-dominated schedules are now calculated with the sets of feasible weights according to equations (10) and (11). The intervals are depicted in Figures 7(a) and 7 Because there does not exist a unique preferred schedule on the basis of the dominance relations or the decision rules, the DM may wish to consider also the performance indicators listed in Section 3.1.…”

Section: Decision Support Phasementioning

confidence: 99%

Maintenance scheduling of a fleet of fighter aircraft through multi-objective simulation-optimization

Mattila

Virtanen

2014

SIMULATION

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The maintenance scheduling problem of a fleet of fighter aircraft is considered through multi-objective simulationoptimization (MOSO). In the problem, a maintenance schedule consisting of target starting times of the maintenance activities of the aircraft is determined. The objectives are to minimize average deviation between the target and actual starting times of the activities and to maximize average aircraft availability. The objectives depend on the maintenance schedule through complex interactions due to a policy in which the need for maintenance is based on the flight hours of the aircraft cumulated during flight missions. In addition, the durations of the flight missions, maintenance activities, and failure repairs are uncertain. Therefore, an MOSO approach is applied to the problem. The approach includes a discrete-event simulation model and a state-of-the art multi-objective simulated annealing algorithm for determining non-dominated schedules. Moreover, a multi-attribute value (MAV) function is used for supporting a maintenance decision-maker (DM) in selecting the preferred non-dominated schedule for implementation. The MAV function captures incomplete information on the values of the objectives as well as on the DM's preference statements regarding the weights of the objectives. The approach is implemented as an MOSO tool whereby the DM can consider the complex interactions and uncertainties of the problem which have not been addressed in the existing literature on maintenance scheduling. The approach and the tool are illustrated with a set of test problems as well as a real-life example problem.

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Section: Decision Support Phasementioning

confidence: 99%

Maintenance scheduling of a fleet of fighter aircraft through multi-objective simulation-optimization

Mattila

Virtanen

2014

SIMULATION

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“…The Military Flight and Maintenance Planning (MFMP) problem is a variant of the better known Civil Flight and Maintenance Planning (FMP) problem where all aircraft return to the base after each flight and fleet availability is prioritized over cost reduction. Initial work on the military variant was done by Sgaslik (1994) and since then different planning horizons have been studied: short term [e.g., Marlow and Dell (2017), Cho (2011), Vojvodić et al (2010)], medium term [e.g., Seif and Yu (2018), Verhoeff et al (2015), Kozanidis (2008), Hahn and Newman (2008), Pippin (1998)] and long term [e.g., Peschiera et al (2020)].…”

Section: State Of the Artmentioning

confidence: 99%

“…Each check requires a certain number of worker-hours and is scheduled every 200-400 flight hours. A Mixed-Integer Linear Programming (MILP) model was formulated in Kozanidis (2008) to solve simulated instances of 6-month horizons and a fleet of up to 30 aircraft. This model was applied in Gavranis and Kozanidis (2015) within an efficient solving method for a particular objective function case (maximizing overall sustainability), and Seif and Yu (2018) expanded this work to apply it for multiple check types and stations and a heterogeneous fleet.…”

Section: State Of the Artmentioning

confidence: 99%

A novel solution approach with ML-based pseudo-cuts for the Flight and Maintenance Planning problem

et al. 2020

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This paper deals with the long-term Military Flight and Maintenance Planning problem. In order to solve this problem efficiently, we propose a new solution approach based on a new Mixed Integer Program and the use of both valid cuts generated on the basis of initial conditions and learned cuts based on the prediction of certain characteristics of optimal or near-optimal solutions. These learned cuts are generated by training a Machine Learning model on the input data and results of 5000 instances. This approach helps to reduce the solution time with little losses in optimality and feasibility in comparison with alternative matheuristic methods. The obtained experimental results show the benefit of a new way of adding learned cuts to problems based on predicting specific characteristics of solutions.

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“…Wei employed the game‐theoretical model to investigate how airport landing fees could influence airlines' decisions on aircraft size and service frequency. Kozanidis developed a multiobjective optimization model to maximize the availability of the aircraft. He showed that flight and maintenance requirements are two important factors for fleet planning while Givoni and Rietveld analyzed the environmental impacts of airlines' choice on aircraft size.…”

Section: Introductionmentioning

confidence: 99%

An optimal aircraft fleet management decision model under uncertainty

Khoo

Teoh

2013

J of Advced Transportation

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SUMMARY Decision planning for an efficient fleet management is crucial for airlines to ensure a profit while maintaining a good level of service. Fleet management involves acquisition and leasing of aircraft to meet travelers' demand. Accordingly, the methods used in modeling travelers' demand are crucial as they could affect the robustness and accuracy of the solutions. Compared with most of the existing studies that consider deterministic demand, this study proposes a new methodology to find optimal solutions for a fleet management decision model by considering stochastic demand. The proposed methodology comes in threefold. First, a five‐step modeling framework, which is incorporated with a stochastic demand index (SDI), is proposed to capture the occurrence of uncertain events that could affect the travelers' demand. Second, a probabilistic dynamic programming model is developed to optimize the fleet management model. Third, a probable phenomenon indicator is defined to capture the targeted level of service that could be achieved satisfactorily by the airlines under uncertainty. An illustrative case study is presented to evaluate the applicability of the proposed methodology. The results show that it is viable to provide optimal solutions for the aircraft fleet management model. Copyright © 2013 John Wiley & Sons, Ltd.

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A multiobjective model for maximizing fleet availability under the presence of flight and maintenance requirements

Cited by 21 publications

References 9 publications

Maintenance scheduling of a fleet of fighter aircraft through multi-objective simulation-optimization

Maintenance scheduling of a fleet of fighter aircraft through multi-objective simulation-optimization

A novel solution approach with ML-based pseudo-cuts for the Flight and Maintenance Planning problem

An optimal aircraft fleet management decision model under uncertainty

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