A heuristic maintenance scheduling framework for a military aircraft fleet under limited maintenance capacities

被引:15
|
作者
Zhang, Qin [1 ]
Liu, Yu [1 ,2 ,3 ]
Xiahou, Tangfan [1 ,2 ]
Huang, Hong-Zhong [1 ,2 ]
机构
[1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Ctr Syst Reliabil & Safety, Chengdu 611731, Sichuan, Peoples R China
[3] 2006 Xiyuan Ave, Chengdu, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
Military aircraft fleet; Maintenance scheduling; Limited maintenance capacities; Iterated greedy heuristic; Horizon splitting iterated greedy heuristic; ITERATED GREEDY ALGORITHM; SELECTIVE MAINTENANCE; GAME APPROACH; SYSTEMS; AVAILABILITY; OPTIMIZATION; FLIGHT; STRATEGY; MACHINE; MODELS;
D O I
10.1016/j.ress.2023.109239
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In many military scenarios, an aircraft fleet is oftentimes required to execute a sequence of missions with finite breaks between two adjacent missions. The performance of a fleet can be significantly improved by conducting appropriate maintenance activities on damaged aircraft during each break. The maintenance scheduling of such a scenario is, however, inherently subject to the limitation of maintenance capacities, such as repairpersons and repair facilities. Each maintenance activity contains multiple maintenance jobs that must be sequentially carried out, whereas the structural dependence of the constituent parts of each aircraft produces additional constraints to the sequence of maintenance activities. In this article, a new maintenance scheduling framework for a fleet of military aircraft is formulated to maximize the expected fleet readiness. Each maintenance activity requires specific repair facilities and repairpersons, and the limited maintenance capacities have to be arranged in a holistic manner. Due to the unexpected arrival of the next mission on battlefields, the uncertainty associated with the time duration of a break is also considered. Two heuristic algorithms are put forth to resolve the resulting optimization problem efficiently. Comparative studies show that the proposed heuristics outperform other alternative algorithms and are promising to solve large-scale problems.
引用
收藏
页数:16
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