Abstract
Airline rescheduling can minimize the number of abnormal flights and their subsequent adverse effects during the flight-plan execution process. The current subjective manual rescheduling method has a low efficiency and does not consider uncontrollable factors. The traditional machine learning-based methods require massive training data, which are not applicable to some few sample's factors. In this study, we firstly addressed the factors that influence airline rescheduling. A set of airline rescheduling indicators was determined as the inputs of a multilayer perceptron model. Then, an airline rescheduling algorithm, namely MAML–SGD was proposed to solve the mentioned model constraint with few samples. Compared with the identification model that uses an SGD algorithm to iteratively update parameters, this proposed algorithm can maintain the stability of the gradient descent during the training and testing processes of the model. Finally, by using historical data from Guangzhou Baiyun International Airport and Urumqi Diwopu International Airport, the airline rescheduling accuracy of the model reached 97%, which was markedly higher than the results obtained by traditional machine learning models such as SVM.
Similar content being viewed by others
Availability of data and materials
The data underlying this article cannot be shared publicly because the relevant units providing the data require the confidentiality of the data content. The data will be shared on reasonable request to the corresponding author.
References
Findlay CC (1983) Optimal air fares and flight frequency and market results. J Transp Econ Policy 17(1):49–66
Teodorović DB (1983) Flight frequency determination. J Transp Eng 109:747–757
Jafari N, Zegordi SH (2010) The airline perturbation problem: considering disrupted passengers. Transp Plan Technol 33:203–220
Jafari N, Zegordi SH (2011) Simultaneous recovery model for aircraft and passengers. J Frankl Inst 348:1638–1655
Bai F (2010) Research on aircraft and crew rescheduling problems of irregular flight. Nanjing University of Aeronautics and Astronautics, Nanjing
Libo Zhang HB (2013) Disrupted flight scheduling model based on the time-band network. Syst Eng 31:60–68
Petersen JD, Sölveling G, Clarke J-P, Johnson EL, Shebalov S (2012) An optimization approach to airline integrated recovery. Transp Sci 46:482–500
Bo Z, Jinfu Z, Weiwei W (2016) Two stage stochastic programming in aircraft routing recovery problem. J Wut (Inf Manag Eng) 38:591–596+601
Arıkan U, Gürel S, Aktürk MS (2017) Flight network-based approach for integrated airline recovery with cruise speed control. Transp Sci 51:1259–1287
Liang Z et al (2018) A column generation-based heuristic for aircraft recovery problem with airport capacity constraints and maintenance flexibility. Transp Res Part B Methodol 113:70–90
Woo Y-B, Moon I (2021) Scenario-based stochastic programming for an airline-driven flight rescheduling problem under ground delay programs. Transp Res Part E Logist Transp Rev 150:102360
Ji C, Gao M, Zhang X, Li J (2021) A novel rescheduling algorithm for the airline recovery with flight priorities and airport capacity constraints. Asia-Pac J Oper Res 38:2140025
Yan S, Chen Y-C (2022) Flight rescheduling, fleet rerouting and passenger reassignment for typhoon disruption events. Transp Lett 14:818–837
Lambelho M, Mitici M, Pickup S, Marsden A (2020) Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions. J Air Transp Manag 82:101737
Deng W, Li K, Zhao H (2023) A flight arrival time prediction method based on cluster clustering-based modular with deep neural network. IEEE Trans Intell Transp Syst 1–10. https://doi.org/10.1109/TITS.2023.3338251
Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, vol 29
Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol 30
Ravi S, Larochelle H (2017) Optimization as a model for few-shot learning. In: International Conference on Learning Representations
Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp 1126–1135
of China CAA (2021) Qinformation on consumer complaints in air transport in 2020. http://www.icscc.org.cn/content/details_132_3256.html
Rodríguez-Sanz Á, Cano J, Fernández BR (2021) Impact of weather conditions on airport arrival delay and throughput. Aircr Eng Aerosp Technol 94:60–78
Reiche C, Cohen AP, Fernando C (2021) An initial assessment of the potential weather barriers of urban air mobility. IEEE Trans Intell Transp Syst 22:6018–6027
Yu C-K, Cheng L-W, Wu C-C, Tsai C-L (2020) Outer tropical cyclone rainbands associated with typhoon Matmo (2014). Mon Weather Rev 148:2935–2952
Teodorović D, Stojković G (1990) Model for operational daily airline scheduling. Transp Plan Technol 14:273–285
Subramanya K, Kermanshachi S (2021) Impact of covid-19 on transportation industry: comparative analysis of road, air, and rail transportation modes. In: International Conference on Transportation and Development 2021, pp 230–242
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133
Rosenblatt F (1961) Principles of neurodynamics. Perceptrons and the theory of brain mechanisms. Technical report, Cornell Aeronautical Lab Inc, Buffalo, NY
LeCun Y, Touresky D, Hinton G, Sejnowski T (1988) A theoretical framework for back-propagation. In: Proceedings of the 1988 Connectionist Models Summer School, vol 1, pp 21–28
Amari S-I (1993) Backpropagation and stochastic gradient descent method. Neurocomputing 5:185–196
Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp 1126–1135
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
McCallum A, Nigam K et al (1998) A comparison of event models for Naive Bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, vol 752, pp 41–48
Quinlan JR (2014) C4. 5: programs for machine learning (Amsterdam, Boston)
Funding
Supported by the Civil Aviation Joint Research Fund of the National Natural Science Foundation of China and the Civil Aviation Administration of China (U2233208) Supported by the Postgraduate Research & Practice Innovation Program of NUAA(xcxjh20220720).
Author information
Authors and Affiliations
Contributions
ZS was contributed to conceptualization, methodology, software, validation, supervision, project administration, writing—review and editing. QZ was contributed to conceptualization, methodology, software, visualization, formal analysis, data curation, writing—original draft. YL was contributed to conceptualization, methodology, formal analysis, writing—editing.
Corresponding author
Ethics declarations
Conflict of interest
We declare that the authors have no conflict of interest as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.
Ethical approval
This declaration is not applicable for this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shen, Z., Zhao, Q. & Liu, Y. MAML–SGD: a reliable airline rescheduling algorithm for small-sample learning based on MAML and SGD. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06014-y
Accepted:
Published:
DOI: https://doi.org/10.1007/s11227-024-06014-y