Abstract
Working overtime is a problem that airlines and maintenance technicians pay great attention to and have not been effectively solved. To alleviate this phenomenon as well as save the maintenance cost and time, this paper introduces a flexible task splitting strategy (FTSS) into the original aircraft maintenance technician scheduling (AMTS) model and presents a novel model called AMTS-FTSS. In AMTS-FTSS, one maintenance task can be completed by multiple multi-skilled maintenance technicians. When the maintenance task needs to be completed over time, it can be flexibly split, and the splitting standard is controlled by the flexible time. Finally, ant colony optimization, particle swarm optimization, bacterial foraging optimization, and artificial bee colony algorithms and their variants are used to verify the effectiveness and universality of FTSS. The experimental results show that compared with the AMTS model, AMTS-FTSS can save the maintenance cost and time in most circumstances.
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Acknowledgment
The study is supported by The National Natural Science Foundation of China (Nos. 71971143, 61703102), Major Project of Natural Science Foundation of China (No. 71790615), Integrated Project of Natural Science Foundation of China (No. 91846301), Social Science Youth Foundation of Ministry of Education of China (Nos. 21YJC630052, 21YJC630181), Key Research Foundation of Higher Education of Guangdong Provincial Education Bureau (No. 2019KZDXM030), Natural Science Foundation of Guangdong Province (Nos. 2020A1515010749, 2020A1515010752), Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515110401), and Natural Science Foundation of Shenzhen City (No. JCYJ20190808145011259), Guangdong Province Innovation Team (No. 2021WCXTD002).
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Xue, B., Zhong, H., Lu, J., Zhou, T., Niu, B. (2023). Flexible Task Splitting Strategy in Aircraft Maintenance Technician Scheduling Based on Swarm Intelligence. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13655. Springer, Cham. https://doi.org/10.1007/978-3-031-20096-0_2
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