Skip to main content
SpringerLink
Log in

Artificial Bee Colony Algorithm for Solving the Flight Disruption Problem

  • Conference paper
  • First Online:
Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection (PAAMS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 887))

Abstract

This paper presents the optimization algorithm Artificial Bee Colony (ABC) firstly introduced by in 2005 and proposed for optimizing numerical problems. ABC is the swarm-based meta-heuristic algorithm inspired by intelligent behavior of honey bee colonies. In this paper, ABC has been applied on solving the flight disruption problem, by swapping aircraft and/or cancelling/delaying flights, and its performance has been shown through experimentation. The environment and data for experiments are provided by MASDIMA, Multi-Agent System for DIsruption MAnagement developed by LIACC (Laboratory of Artificial Intelligence and Computer Science).

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Similar content being viewed by others

References

  1. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  2. Chong, C.S., Sivakumar, A.I., Malcolm Low, Y.H., Gay, K.L.: A bee colony optimization algorithm to job shop scheduling. In: Proceedings of the 38th Conference on Winter Simulation WSC 2006, California, pp. 1954–1961 (2006)

    Google Scholar 

  3. Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm. Technical report, Manufacturing Engineering Centre, Cardiff University, UK (2005)

    Google Scholar 

  4. Lucic, P., Teodorovic, D.: Bee system: modeling combinatiorial optimization transportation engineering problems by swarm intelligence (2001)

    Google Scholar 

  5. Castro, A.J.M., Rocha, A.P., Oliveira, E.: A New Approach for Disruption Management in Airline Operations Control. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43373-7

    Book  Google Scholar 

  6. Tereshko, V., Loengarov, A.: Collective decision-making in honey bee foraging dynamics. Comput. Inf. Syst. 9(3), 1–7 (2005). University of the West of Scotland, UK

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Informatics, Vienna University of Technology, Vienna, Austria

    Tanja Šarčević

  2. LIACC, DEI/FEUP, University of Porto, ‎Porto, Portugal

    Ana Paula Rocha

  3. LIACC, University of Porto, ‎Porto, Portugal

    Antonio J. M. Castro

Authors
  1. Tanja Šarčević
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ana Paula Rocha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio J. M. Castro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonio J. M. Castro .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    Javier Bajo

  2. Departamento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain

    Juan M. Corchado

  3. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Elena María Navarro Martínez

  4. TECNALIA, Derio, Spain

    Eneko Osaba Icedo

  5. Lille University of Science and Technology, Villeneuve d’Ascq, France

    Philippe Mathieu

  6. Poznan University of Technology, Poznan, Poland

    Patrycja Hoffa-Dąbrowska

  7. Polytechnic University of Valencia, Valencia, Spain

    Elena del Val

  8. Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada

    Sylvain Giroux

  9. University of Porto, Porto, Portugal

    Antonio J.M. Castro

  10. Rio de Janeiro State University, Rio de Janeiro, Brazil

    Nayat Sánchez-Pi

  11. Polytechnic University of Valencia, Valencia, Spain

    Vicente Julián

  12. Informatica e Estatistica, Universidade Federal de Santa Catar, Florianopolis, Santa Catarina, Brazil

    Ricardo Azambuja Silveira

  13. Universidad Rey Juan Carlos, Móstoles, Madrid, Spain

    Alberto Fernández

  14. ICB, Universität Duisburg-Essen, Essen, Germany

    Rainer Unland

  15. Facultad de Informática, Universidad Complutense de Madrid, Madrid, Spain

    Rubén Fuentes-Fernández

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Šarčević, T., Rocha, A.P., Castro, A.J.M. (2018). Artificial Bee Colony Algorithm for Solving the Flight Disruption Problem. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Communications in Computer and Information Science, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-94779-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-94779-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94778-5

  • Online ISBN: 978-3-319-94779-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation