Abstract
This paper studies the problem of aircraft maintenance technician scheduling problem. Aircraft maintenance companies often need to allocate aircraft maintenance technicians in advance according to maintenance orders before carrying out maintenance work, with the aim of maximizing the company's benefits. In order to solve the aircraft maintenance technician scheduling problem, we propose a reorganized bacterial foraging optimization algorithm (RBFO), which introduces the individual information transmission mechanisms among each individual in the bacterial swarm, and reorganizes the structure of the original bacterial foraging algorithm. The experimental results verify the applicability of the proposed algorithm in the specific constructed model, and give the optimal task-technician allocation scheme based on the numerical example data. The performance of RBFO is high-lighted through comparative experiments.
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References
IATA Economics. https://www.iata.org/publications/economics/. Accessed 26 Feb 2021
Qin, Y., Zhang, J., Chan, F., Chung, S., Qu, T.: A two-stage optimization approach for aircraft hangar maintenance planning and staff assignment problems under MRO outsourcing mode. Comput. Ind. Eng. 146, 106607 (2020)
Marcontell, D.: MRO’s offshore edge shrinking. Aviation Week Space Technol. 175(22), 56 (2013)
Salazar-Gonzalez, J.: Approaches to solve the fleet-assignment, aircraft-routing, crew-pairing and crew-rostering problems of a regional carrier. Omega 43, 71–82 (2014)
Quesnel, F., Desaulniers, G., Soumis, F.: Improving air crew rostering by considering crew preferences in the crew pairing problem. Transp. Sci. 54(1), 97–114 (2019)
Zhang, Z., Zhou, M.C., Wang, J.: Construction-based optimization approaches to airline crew rostering problem. IEEE Trans. Autom. Sci. Eng. 17(3), 1399–1409 (2020)
Permatasari, C.I., Sutopo, W., Hisjam, M.: Aircraft maintenance manpower shift planning with multiple aircraft maintenance licenced. IOP Conf. Ser.: Mater. Sci. Eng. 495, 012023 (2019)
Garcia, J., Altimiras, F., Pena, A., Astorga, G., Peredo, O.: A binary cuckoo search big data algorithm applied to large-scale crew scheduling problems. Complexity 2018, 1–15 (2018)
Gutjahr, W.J., Rauner, M.S.: An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria. Comput. Oper. Res. 34(3), 642–666 (2007)
Niu, B., Wang, C., Liu, J., Gan, J., Yuan, L.: Improved bacterial foraging optimization algorithm with information communication mechanism for nurse scheduling. In: Huang, D.-S., Jo, K.-H., Hussain, A. (eds.) ICIC 2015. LNCS, vol. 9226, pp. 701–707. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22186-1_69
Passino, K.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)
Niu, B., Wang, H.: Bacterial colony optimization. Discrete Dyn. Nature Soc. 2012, 698057 (2012)
Gang, C., Wen, H., Lawrence, C., Tan, L., Han, Y.: Assigning licenced technicians to maintenance tasks at aircraft maintenance base: a bi-objective approach and a Chinese airline application. Int. J. Prod. Res. 55, 19–20 (2017)
Acknowledgement
This work is supported in part by the National Natural Science Foundation of China under Grant 71971143, and in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515110401, and in part by Key Projects of Colleges and Universities in Guangdong Province under Grant 2019KZDXM030.
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Niu, B., Xue, B., Zhou, T., Zhang, C., Xiao, Q. (2021). Reorganized Bacterial Foraging Optimization Algorithm for Aircraft Maintenance Technician Scheduling Problem. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_45
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DOI: https://doi.org/10.1007/978-3-030-78743-1_45
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