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Optimizing Airline Crew Scheduling Using Biased Randomization: A Case Study

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Advances in Artificial Intelligence (CAEPIA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9868))

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Abstract

Various complex decision making problems are related to airline planning. In the competitive airline industry, efficient crew scheduling is hereby of major practical importance. This paper presents a metaheuristic approach based on biased randomization to tackle the challenging Crew Pairing Problem (CPP). The objective of the CPP is the establishment of flight pairings allowing for cost minimizing crew-flight assignments. Experiments are done using a real-life case with different constraints. The results show that our easy-to-use and fast algorithm reduces overall crew flying times and the necessary number of accompanying crews compared to the pairings currently applied by the company.

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Author information

Authors and Affiliations

  1. Public University of Navarra, Pamplona, Spain

    Alba Agustín

  2. Open University of Catalonia, Barcelona, Spain

    Aljoscha Gruler, Jesica de Armas & Angel A. Juan

Authors
  1. Alba Agustín
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  2. Aljoscha Gruler
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  3. Jesica de Armas
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  4. Angel A. Juan
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Corresponding author

Correspondence to Aljoscha Gruler .

Editor information

Editors and Affiliations

  1. Artificial Intelligence Center, University of Oviedo, Gijón, Spain

    Oscar Luaces

  2. University of Castilla-La Mancha , Albacete, Spain

    José A. Gámez

  3. Public University of Navarre , Pamplona, Spain

    Edurne Barrenechea

  4. Universidad Pablo de Olavide , Sevilla, Spain

    Alicia Troncoso

  5. Public University of Navarre , Pamplona, Navarra, Spain

    Mikel Galar

  6. University of Salamanca , Salamanca, Spain

    Héctor Quintián

  7. University of Salamanca , Salamanca, Spain

    Emilio Corchado

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Agustín, A., Gruler, A., de Armas, J., Juan, A.A. (2016). Optimizing Airline Crew Scheduling Using Biased Randomization: A Case Study. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_31

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  • DOI: https://doi.org/10.1007/978-3-319-44636-3_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44635-6

  • Online ISBN: 978-3-319-44636-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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