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Airline crew scheduling: models, algorithms, and data sets

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EURO Journal on Transportation and Logistics

Abstract

The airline crew scheduling problem has received extensive attention, particularly in the last 60 years. This problem is frequently divided into crew pairing and crew assignment because of its large size and the complex safety agreements and contractual rules. Several solution methodologies have been developed, but many objectives and constraints are treated approximately and research is ongoing. In this paper, we present a comprehensive problem definition for the airline crew scheduling problem, and we review existing problem formulations and solution methodologies. In addition, we formulate the personalized cockpit crew scheduling problem as a set covering problem and we solve it using column generation. We present computational results for real data from a major US carrier, and we describe the data sets (available on the internet) in detail to establish a basis for future research.

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Acknowledgments

This research was supported by the Natural Sciences and Engineering Research Council of Canada and a collaborative R&D grant from AD OPT, a division of Kronos. Thanks are due to the personnel of AD OPT, a division of Kronos, for providing the data sets and the GENCOL software library. The authors are thankful to Frédéric Quesnel for his help in preparing the data sets and generators. The authors are grateful to the editor and two anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. Department of Mathematics and Industrial Engineering, GERAD and École Polytechnique de Montréal, Succ. Centre-Ville, C.P. 6079, Montreal, QC, H3C 3A7, Canada

    Atoosa Kasirzadeh, Mohammed Saddoune & François Soumis

  2. Université Hassan II, Faculté des Sciences et Techniques de Mohammedia, BP 146, 20650, Mohammedia, Morocco

    Mohammed Saddoune

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  1. Atoosa Kasirzadeh
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  3. François Soumis
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Correspondence to Atoosa Kasirzadeh.

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Kasirzadeh, A., Saddoune, M. & Soumis, F. Airline crew scheduling: models, algorithms, and data sets. EURO J Transp Logist 6, 111–137 (2017). https://doi.org/10.1007/s13676-015-0080-x

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