A bi-objective aircraft maintenance routing problem based on flying hours to efficient use of available fleet

Purpose The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours threshold is met. After receiving maintenance service, the model ignores previous flying hours and the aircraft can kee...

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Veröffentlicht in:Journal of facilities management Jg. 22; H. 2; S. 325 - 344
Hauptverfasser: Esmaeilzadeh, Hiwa, Rashidi Komijan, Alireza, Kazemipoor, Hamed, Fallah, Mohammad, Tavakkoli-Moghaddam, Reza
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bingley Emerald Publishing Limited 14.03.2024
Emerald Group Publishing Limited
Schlagworte:
Aircraft
Airlines
Assignment problem
Aviation
Costs
Genetic algorithms
Integer programming
Monte Carlo simulation
Optimization
Pilots
Schedules
Scheduling
Traveling salesman problem
Epsilon constraint
Aircraft maintenance routing problem
Bi-objective mixed integer programming
Airline operations
NSGA-II mathematical modeling
ISSN:1472-5967, 1741-0983, 1472-5967
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Zusammenfassung:Purpose The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours threshold is met. After receiving maintenance service, the model ignores previous flying hours and the aircraft can keep on flying until the threshold value is reached again. Moreover, the model considers aircraft age and efficiency to assign them to flights. Design/methodology/approach The aircraft maintenance routing problem (AMRP), as one of the most important problems in the aviation industry, determines the optimal route for each aircraft along with meeting maintenance requirements. This paper presents a bi-objective mixed-integer programming model for AMRP in which several criteria such as aircraft efficiency and ferrying flights are considered. Findings As the solution approaches, epsilon-constraint method and a non-dominated sorting genetic algorithm (NSGA-II), including a new initializing algorithm, are used. To verify the efficiency of NSGA-II, 31 test problems in different scales are solved using NSGA-II and GAMS. The results show that the optimality gap in NSGA-II is less than 0.06%. Finally, the model was solved based on real data of American Eagle Airlines extracted from Kaggle datasets. Originality/value The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1472-5967
1741-0983
1472-5967
DOI:10.1108/JFM-02-2022-0018