期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 169, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.114399
关键词
Aircraft routing problem; Sequential decision-making problem; Markov Decision Process (MDP); Reinforcement Learning
类别
资金
- National Natural Science Foundation of China [71901052]
With fierce competition in the airline industry, the study focuses on generating optimal maintenance routes for aircraft. A network flow-based ILP framework considering multiple maintenance constraints and a reinforcement learning algorithm have been proposed to efficiently solve the operational aircraft maintenance routing problem.
With recent developments in the airline industry worldwide, the competition among the industry has increased largely with many key players in the market. In order to generate profits, the industry has paid much attention to generate optimal routes that are maintenance feasible. The main aim of operational aircraft maintenance routing problem (OAMRP) is to generate these optimal routes for each aircraft that are maintenance feasible and follow the constraints defined by the Federal Aviation Administration (FAA). In this paper, the OAMRP is studied with two main objectives. First, to propose a formulation of a network flow-based Integer Linear Programming (ILP) framework for the OAMRP that considers three main maintenance constraints simultaneously: maximum flyinghour, limit on the number of take-offs between two consecutive maintenance checks and the work-force capacity. Second, to develop a new reinforcement learning-based algorithm which can be used to solve the problem, quickly and efficiently, as compared to commonly available optimization software. Finally, the evaluation of the proposed algorithm on real case datasets obtained from a major airline located in the Middle East verifies that the algorithm generates high-quality solutions quickly for both medium and large-scale flight schedule dataset.
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