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Irregular Flight Timetable Recovery Under COVID-19: An Approach Based on Genetic Algorithm

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Data Mining and Big Data (DMBD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1453))

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Abstract

With the growing demands on civil aviation transportation in post-pandemic of COVID-19, irregular flight has become a headache problem for both airlines and passengers. This paper considers the large-scale irregular flight timetable recovery problem for the airline with temporarily closed airport. First, a mathematical model with the objective of minimizing total delay time of passengers under several realistic constraints is constructed. Second, both improved genetic algorithm for the irregular flight timetable recovery problem and encoding scheme are proposed based on problem characteristics. Finally, a large-scale data set from contest is chosen and both optimal solution and recovery scheme are obtained to illustrate the feasibility of our recovery algorithm for irregular flight timetable recovery problem.

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Acknowledgement

The study was supported in part by Natural Science Foundation of China (No. 62103286, 71971143, 71571120, 71702111), in part by Natural Science Foundation of Guangdong Province (No. 2020A1515010749), in part by Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515110401), in part by Key Research Foundation of Higher Education of Guangdong Provincial Education Bureau (No. 2019KZDXM030), and in part by Natural Science Foundation of Shenzhen (No. JCYJ20190808145011259).

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Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Tianwei Zhou, Junrui Lu, Wenwen Zhang, Pengcheng He & Ben Niu

  2. Greater Bay Area International Institute for Innovation, Shenzhen University, Shenzhen, 518060, China

    Tianwei Zhou, Junrui Lu, Wenwen Zhang & Ben Niu

Authors
  1. Tianwei Zhou
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  2. Junrui Lu
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  3. Wenwen Zhang
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  4. Pengcheng He
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  5. Ben Niu
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Corresponding author

Correspondence to Junrui Lu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. The University of Sydney, Sydney, Australia

    Albert Zomaya

  4. Guangzhou University, Guangzhou, China

    Hongyang Yan

  5. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

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Zhou, T., Lu, J., Zhang, W., He, P., Niu, B. (2021). Irregular Flight Timetable Recovery Under COVID-19: An Approach Based on Genetic Algorithm. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_22

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  • DOI: https://doi.org/10.1007/978-981-16-7476-1_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7475-4

  • Online ISBN: 978-981-16-7476-1

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