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Rail-Freight Crew Scheduling with a Genetic Algorithm

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Research and Development in Intelligent Systems XXXI (SGAI 2014)

Abstract

This article presents a novel genetic algorithm designed for the solution of the Crew Scheduling Problem (CSP) in the rail-freight industry. CSP is the task of assigning drivers to a sequence of train trips while ensuring that no driver’s schedule exceeds the permitted working hours, that each driver starts and finishes their day’s work at the same location, and that no train routes are left without a driver. Real-life CSPs are extremely complex due to the large number of trips, opportunities to use other means of transportation, and numerous government regulations and trade union agreements. CSP is usually modelled as a set-covering problem and solved with linear programming methods. However, the sheer volume of data makes the application of conventional techniques computationally expensive, while existing genetic algorithms often struggle to handle the large number of constraints. A genetic algorithm is presented that overcomes these challenges by using an indirect chromosome representation and decoding procedure. Experiments using real schedules on the UK national rail network show that the algorithm provides an effective solution within a faster timeframe than alternative approaches.

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Acknowledgments

This research has been supported by Sheffield Business School at Sheffield Hallam University. The authors would like to thank DB-Schenker Rail (UK) for the provision of data and information about real-world crew scheduling operations.

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Authors and Affiliations

  1. Sheffield Business School, Sheffield Hallam University, Howard Street, Sheffield, S1 1WB, UK

    E. Khmeleva, A. A. Hopgood, L. Tipi & M. Shahidan

Authors
  1. E. Khmeleva
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  2. A. A. Hopgood
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  3. L. Tipi
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  4. M. Shahidan
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Corresponding author

Correspondence to E. Khmeleva .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, United Kingdom

    Max Bramer

  2. School of Computing, Eng & Mathematics, University of Brighton, Brighton, West Sussex, United Kingdom

    Miltos Petridis

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Khmeleva, E., Hopgood, A.A., Tipi, L., Shahidan, M. (2014). Rail-Freight Crew Scheduling with a Genetic Algorithm. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXI. SGAI 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-12069-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-12069-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12068-3

  • Online ISBN: 978-3-319-12069-0

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