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Genetic and memetic algorithms for scheduling railway maintenance activities

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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    Keywords

    genetic algorithm; heuristics; maintenance optimization; memetic algorithm; opportunities;
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