Abstract
Taking the airport de-icing resources optimization arrangements as the research object. When the airport de-icing resources are tense and large-scale delays occur at the airport, airport decision-makers need to consider both fairness and efficiency. At present, the FCFS method is widely used at home and abroad for de-icing scheduling, which has certain defects in fairness and efficiency. To improve the efficiency of airport de-icing and the fairness of resource allocation, through research and analysis of the theory and method of airport de-icing process scheduling problem, a multi-objective mathematical optimization model with the minimum number of strands as the efficiency goal and the minimum weighted dissatisfaction value as the fairness goal is established. An algorithm based on fully combination thinking is designed, and a multi-objective decision-making strategy is proposed for decision makers to choose a satisfactory solution as the airport de-icing resource allocation scheme. Based on the algorithm simulation of the model and comparative analysis, the results show that this algorithm is able to solve the problem of aircraft de-icing scheduling problem, which can improve the resource utilization efficiency better compared with the existing manual scheduling method and ensure the fairness of resource allocation to a certain extent.
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References
Voulgarellis, P.G., Christodoulou, M.A., Boutalis, Y.S.: A MATLAB based simulation language for aircraft ground handling operations at hub airports (SLAGOM). In: Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation, pp. 334–339. IEEE (2005)
Lin, B.M.T., Lu, C.Y., Shyu, S.J., Tsai, C.Y.: Development of new features of ant colony optimization for flow shop scheduling. Int. J. Prod. Econ. 112(2), 742–755 (2007)
Juran, I., Prashker, J.N., Bekhor, S.: A dynamic traffic assignment model for the assessment of moving bottlenecks. Transp. Res. Part C Emerg. Technol. 17(3), 240–258 (2009)
Mao, X.Y., Mors, A.T., Roos, N., et al.: Agent-based scheduling for aircraft deicing. In:Proceedings of the 18th Belgium-Netherlands Conference on Artificial Intelligence, Brussels, pp. 229–236 (2007)
Vitoriano, B., Ortuño, M.T., Tirado, G., et al.: A multi-criteria optimization model for humanitarian aid distribution. J. Global Optim. 51(2), 189–208 (2011)
Huang, M., KarenSmilowitz, B.: Models for relief routing: Equity, efficiency and efficacy. Transp. Res. Part E 48(1), 2–18 (2011)
Bertsimas, D., Farias, V.F., Nikolaos, T.: On the efficiency-fairness trade-off. Manage. Sci. 58(12), 2234–2250 (2012)
Acknowledgments
The research was partially supported by the National Social Science Fund Project, China (No. 16BTQ065) “Multi-source intelligence fusion research on emergencies in big data environment”.
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Guo, Q., Li, B., Luo, X. (2019). Study on Airport De-icing Schedule Problem Balancing Fairness and Efficiency. In: Liu, X., et al. Service-Oriented Computing – ICSOC 2018 Workshops. ICSOC 2018. Lecture Notes in Computer Science(), vol 11434. Springer, Cham. https://doi.org/10.1007/978-3-030-17642-6_2
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DOI: https://doi.org/10.1007/978-3-030-17642-6_2
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