IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/17513.html
   My bibliography  Save this paper

Genetic and memetic algorithms for scheduling railway maintenance activities

Author

Listed:
  • Budai-Balke, G.
  • Dekker, R.
  • Kaymak, U.

Abstract

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms, memetic algorithms and a two-phase heuristic based on opportunities. The aim of the PMSP is to schedule the (short) routine activities and (long) unique projects for one link in the rail network for a certain planning period such that the overall cost is minimized. To reduce costs and inconvenience for the travellers and operators, these maintenance works are clustered as much as possible in the same time period. The performance of the algorithms presented in this paper are compared with the performance of the methods from an earlier work, Budai et al. (2006), using some randomly generated instances.

Suggested Citation

  • Budai-Balke, G. & Dekker, R. & Kaymak, U., 2009. "Genetic and memetic algorithms for scheduling railway maintenance activities," Econometric Institute Research Papers EI 2009-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:17513
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/17513/EI2009-30.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. I. van Zante--de Fokkert & D. den Hertog & F. J. van den Berg & J. H. M. Verhoeven, 2007. "The Netherlands Schedules Track Maintenance to Improve Track Workers’ Safety," Interfaces, INFORMS, vol. 37(2), pages 133-142, April.
    2. G Budai & D Huisman & R Dekker, 2006. "Scheduling preventive railway maintenance activities," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(9), pages 1035-1044, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Urbani, Michele & Brunelli, Matteo & Punkka, Antti, 2023. "An approach for bi-objective maintenance scheduling on a networked system with limited resources," European Journal of Operational Research, Elsevier, vol. 305(1), pages 101-113.
    2. Baldi, Mauro M. & Heinicke, Franziska & Simroth, Axel & Tadei, Roberto, 2016. "New heuristics for the Stochastic Tactical Railway Maintenance Problem," Omega, Elsevier, vol. 63(C), pages 94-102.
    3. Zhang, Chuntian & Gao, Yuan & Yang, Lixing & Gao, Ziyou & Qi, Jianguo, 2020. "Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 64-92.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peng, Fan & Ouyang, Yanfeng, 2012. "Track maintenance production team scheduling in railroad networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1474-1488.
    2. Sedghi, Mahdieh & Kauppila, Osmo & Bergquist, Bjarne & Vanhatalo, Erik & Kulahci, Murat, 2021. "A taxonomy of railway track maintenance planning and scheduling: A review and research trends," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Tomas Lidén, 2020. "Coordinating maintenance windows and train traffic: a case study," Public Transport, Springer, vol. 12(2), pages 261-298, June.
    4. Baldi, Mauro M. & Heinicke, Franziska & Simroth, Axel & Tadei, Roberto, 2016. "New heuristics for the Stochastic Tactical Railway Maintenance Problem," Omega, Elsevier, vol. 63(C), pages 94-102.
    5. Goel, Asvin & Meisel, Frank, 2013. "Workforce routing and scheduling for electricity network maintenance with downtime minimization," European Journal of Operational Research, Elsevier, vol. 231(1), pages 210-228.
    6. Liu, Gehui & Chen, Shaokuan & Ho, Tinkin & Ran, Xinchen & Mao, Baohua & Lan, Zhen, 2022. "Optimum opportunistic maintenance schedule over variable horizons considering multi-stage degradation and dynamic strategy," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Santos, Rui & Fonseca Teixeira, Paulo & Pais Antunes, Antonio, 2015. "Planning and scheduling efficient heavy rail track maintenance through a Decision Rules Model," Research in Transportation Economics, Elsevier, vol. 54(C), pages 20-32.
    8. Zhang, Chuntian & Gao, Yuan & Yang, Lixing & Gao, Ziyou & Qi, Jianguo, 2020. "Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 64-92.
    9. Michael Patriksson & Ann-Brith Strömberg & Adam Wojciechowski, 2015. "The stochastic opportunistic replacement problem, part II: a two-stage solution approach," Annals of Operations Research, Springer, vol. 224(1), pages 51-75, January.
    10. Saleh, Ali & Remenyte-Prescott, Rasa & Prescott, Darren & Chiachío, Manuel, 2024. "Intelligent and adaptive asset management model for railway sections using the iPN method," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    11. Zhang, Nan & Fouladirad, Mitra & Barros, Anne, 2017. "Maintenance analysis of a two-component load-sharing system," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 67-74.
    12. Bouvard, K. & Artus, S. & Bérenguer, C. & Cocquempot, V., 2011. "Condition-based dynamic maintenance operations planning & grouping. Application to commercial heavy vehicles," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 601-610.
    13. Cats, Oded & Koppenol, Gert-Jaap & Warnier, Martijn, 2017. "Robustness assessment of link capacity reduction for complex networks: Application for public transport systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 544-553.
    14. Robin P. Nicolai & Rommert Dekker, 2008. "Optimal Maintenance of Multi-component Systems: A Review," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 11, pages 263-286, Springer.
    15. Petchrompo, Sanyapong & Parlikad, Ajith Kumar, 2019. "A review of asset management literature on multi-asset systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 181-201.
    16. Vansteenwegen, Pieter & Dewilde, Thijs & Burggraeve, Sofie & Cattrysse, Dirk, 2016. "An iterative approach for reducing the impact of infrastructure maintenance on the performance of railway systems," European Journal of Operational Research, Elsevier, vol. 252(1), pages 39-53.
    17. Zhang, Yongxiang & D'Ariano, Andrea & He, Bisheng & Peng, Qiyuan, 2019. "Microscopic optimization model and algorithm for integrating train timetabling and track maintenance task scheduling," Transportation Research Part B: Methodological, Elsevier, vol. 127(C), pages 237-278.
    18. Mohammadi, Reza & He, Qing & Karwan, Mark, 2021. "Data-driven robust strategies for joint optimization of rail renewal and maintenance planning," Omega, Elsevier, vol. 103(C).
    19. Ashish K. Nemani & Suat Bog & Ravindra K. Ahuja, 2010. "Solving the Curfew Planning Problem," Transportation Science, INFORMS, vol. 44(4), pages 506-523, November.
    20. Budai-Balke, G. & Dekker, R. & Nicolai, R.P., 2006. "A review of planning models for maintenance and production," Econometric Institute Research Papers EI 2006-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    More about this item

    Keywords

    genetic algorithm; heuristics; maintenance optimization; memetic algorithm; opportunities;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ems:eureir:17513. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.