Skip to main content
SpringerLink
Log in

A Tabu-Based Multi-objective Particle Swarm Optimization for Irregular Flight Recovery Problem

  • Conference paper
  • First Online:
Machine Learning for Cyber Security (ML4CS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13657))

Included in the following conference series:

  • 565 Accesses

Abstract

Air transportation is eminent for its fast speed and low cargo damage rate among other ways. However, it is greatly limited by emergent factors like bad weather and current COVID-19 epidemic, where irregular flights may occur. Confronted with the negative impact caused by irregular flight, it is vital to rearrange the preceding schedule to reduce the cost. To solve this problem, first, we established a multi-objective model considering cost and crew satisfaction simultaneously. Secondly, due to the complexity of irregular flight recovery problem, we proposed a tabu-based multi-objective particle swarm optimization introducing the idea of tabu search. Thirdly, we devised an encoding scheme focusing on the characteristic of the problem. Finally, we verified the superiority of the tabu-based multi-objective particle swarm optimization through the comparison against MOPSO by the experiment based on real-world data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Notice on the issuance of Normal Statistical Method of Civil Aviation Flight[R]. Communique of Civil Aviation Administration of China (2003)

    Google Scholar 

  2. Chutima, P., Arayikanon, K.: Many-objective low-cost airline cockpit crew rostering optimisation. Comput. Ind. Eng. 150, 106844 (2020)

    Article  Google Scholar 

  3. Wen, X., Ma, H.L., Chung, S.H., et al.: Robust airline crew scheduling with flight flying time variability. Transp. Res. Part E: Logist. Transp. Rev. 144, 102132 (2020)

    Article  Google Scholar 

  4. Zeighami, V., Saddoune, M., Soumis, F.: Alternating Lagrangian decomposition for integrated airline crew scheduling problem. Eur. J. Oper. Res. 287(1), 211–224 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  5. Zhou, S.Z., Zhan, Z.H., Chen, Z.G., et al.: A multi-objective ant colony system algorithm for airline crew rostering problem with fairness and satisfaction. IEEE Trans. Intell. Transp. Syst. 22(11), 6784–6798 (2020)

    Article  Google Scholar 

  6. Doi, T., Nishi, T., Voß, S.: Two-level decomposition-based matheuristic for airline crew rostering problems with fair working time. Eur. J. Oper. Res. 267(2), 428–438 (2018)

    Google Scholar 

  7. 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 (2020)

    Article  MATH  Google Scholar 

  8. Antunes, D., Vaze, V., Antunes, A.P.: A robust pairing model for airline crew scheduling. Transp. Sci. 53(6), 1751–1771 (2019)

    Article  Google Scholar 

  9. Wen, X., Chung, S.H., Ji, P., et al.: Individual scheduling approach for multi-class airline cabin crew with manpower requirement heterogeneity. Transp. Res. Part E: Logist. Transp. Rev. 163, 102763 (2022)

    Article  Google Scholar 

  10. Evler, J., Lindner, M., Fricke, H., et al.: Integration of turnaround and aircraft recovery to mitigate delay propagation in airline networks. Comput. Oper. Res. 138, 105602 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  11. Jin, H., Chen, S., Ran, X., et al.: Column generation-based optimum crew scheduling incorporating network representation for urban rail transit systems. Comput. Ind. Eng. 169, 108155 (2022)

    Article  Google Scholar 

  12. Zhang, X., Zheng, X., Cheng, R., et al.: A competitive mechanism based multi-objective particle swarm optimizer with fast convergence. Inf. Sci. 427, 63–76 (2018)

    Article  MathSciNet  Google Scholar 

  13. Luo, J., Huang, X., Yang, Y., et al.: A many-objective particle swarm optimizer based on indicator and direction vectors for many-objective optimization. Inf. Sci. 514, 166–202 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  14. Cui, Y., Meng, X., Qiao, J.: A multi-objective particle swarm optimization algorithm based on two-archive mechanism. Appl. Soft Comput. 119, 108532 (2022)

    Article  Google Scholar 

  15. Devaraj, A.F.S., Elhoseny, M., Dhanasekaran, S., et al.: Hybridization of firefly and improved multi-objective particle swarm optimization algorithm for energy efficient load balancing in cloud computing environments. J. Parallel Distrib. Comput. 142, 36–45 (2020)

    Article  Google Scholar 

  16. Qu, B., Li, C., Liang, J., et al.: A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems. Appl. Soft Comput. 86, 105886 (2020)

    Article  Google Scholar 

  17. Liang, J., Guo, Q., Yue, C., Qu, B., Yu, K.: A self-organizing multi-objective particle swarm optimization algorithm for multimodal multi-objective problems. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10941, pp. 550–560. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93815-8_52

    Chapter  Google Scholar 

  18. Liu, J., Zhang, H., He, K., et al.: Multi-objective particle swarm optimization algorithm based on objective space division for the unequal-area facility layout problem. Expert Syst. Appl. 102, 179–192 (2018)

    Article  Google Scholar 

  19. bin Mohd Zain, M.Z., Kanesan, J., Chuah, J.H., et al.: A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization. Appl. Soft Comput. 70, 680–700 (2018)

    Google Scholar 

  20. Goyal, K.K., Sharma, N., Dev Gupta, R., et al.: A soft computing-based analysis of cutting rate and recast layer thickness for AZ31 alloy on WEDM using RSM-MOPSO. Materials 15(2), 635 (2022)

    Article  Google Scholar 

  21. Mahapatra, G.S., Maneckshaw, B., Barker, K.: Multi-objective reliability redundancy allocation using MOPSO under hesitant fuzziness. Expert Syst. Appl. 198, 116696 (2022)

    Article  Google Scholar 

  22. Sellami, R., Farooq, S., Rafik, N.: An improved MOPSO algorithm for optimal sizing & placement of distributed generation: a case study of the Tunisian offshore distribution network (ASHTART). Energy Rep. 8, 6960–6975 (2022)

    Article  Google Scholar 

  23. Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  24. Wei, G., Yu, G., Song, M.: Optimization model and algorithm for crew management during airline irregular operations. J. Comb. Optim. 1(3), 305–321 (1997)

    Article  MATH  Google Scholar 

Download references

Acknowledgment

The study was supported in part by the Natural Science Foundation of China Grant No. 62103286, No. 71971143, No. 62001302, in part by Social Science Youth Foundation of Ministry of Education of China under Grant 21YJC630181, in part by Guangdong Provincial Philosophy and Social Sciences Planning Project under Grant GD22XGL22, in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515110401, 2021A1515011348, 2019A1515111205, in part by Guangdong Province Philosophy and Social Science Planning Discipline Co-construction Project under Grant GD22XGL22, in part by Natural Science Foundation of Guangdong Province under Grant 2020A1515010749, 2020A1515010752, in part by Key Research Foundation of Higher Education of Guangdong Provincial Education Bureau under Grant 2019KZDXM030, in part by Natural Science Foundation of Shenzhen under Grant JCYJ20190808145011259, in part by Shenzhen Science and Technology Program under Grant RCBS20200714114920379, in part by Guangdong Province Innovation Team Intelligent Management and Interdisciplinary Innovation under Grant 2021WCXTD002, in part by Special Projects in Key Fields of Ordinary Colleges and Universities in Guangdong Province under Grant 2022ZDZX2054.

Author information

Authors and Affiliations

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

    Tianwei Zhou, Yichen Lai, Xiaojie Huang, Xumin Chen & Huifen Zhong

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

    Tianwei Zhou

  3. Faculty of Business and Administration, University of Macau, Macau, 999078, China

    Huifen Zhong

Authors
  1. Tianwei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yichen Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaojie Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xumin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huifen Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huifen Zhong .

Editor information

Editors and Affiliations

  1. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, IN, USA

    Yuan Xu

  2. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Huang Teng

  4. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  5. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Jin Li

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, T., Lai, Y., Huang, X., Chen, X., Zhong, H. (2023). A Tabu-Based Multi-objective Particle Swarm Optimization for Irregular Flight Recovery Problem. 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 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20102-8_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20101-1

  • Online ISBN: 978-3-031-20102-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation