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Maintenance Location Routing for Rolling Stock Under Line and Fleet Planning Uncertainty

Author

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  • Denise D. Tönissen

    (School of Business and Economics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands)

  • Joachim J. Arts

    (Luxembourg Centre for Logistics and Supply Chain Management, University of Luxembourg, L-1511 Luxembourg City, Luxembourg; School of Industrial Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, Netherlands)

  • Zuo-Jun (Max) Shen

    (Department of Industrial Engineering and Operations Research, Department of Civil and Environmental Engineering, Tsinghua-Berkeley Shenzhen Institute, University of California, Berkeley, Berkeley, California 94720)

Abstract

Rolling stock needs regular maintenance in a maintenance facility. Rolling stock from different fleets are routed to maintenance facilities by interchanging the destinations of trains at common stations and by using empty drives. We consider the problem of locating maintenance facilities in a railway network under uncertain or changing line planning, fleet planning, and other uncertain factors. These uncertainties and changes are modeled by a discrete set of scenarios. We show that this new problem is NP-hard and provide a two-stage stochastic programming and a two-stage robust optimization formulation. The second-stage decision is a maintenance routing problem with similarity to a minimum cost-flow problem. We prove that the facility location decisions remain unchanged under a simplified routing problem, and this gives rise to an efficient mixed-integer programming (MIP) formulation. This result also allows us to find an efficient decomposition algorithm for the robust formulation based on scenario addition (SA). Computational work shows that our improved MIP formulation can efficiently solve instances of industrial size. SA improves the computational time for the robust formulation even further and can handle larger instances due to more efficient memory usage. Finally, we apply our algorithms on practical instances of the Netherlands Railways and give managerial insights.

Suggested Citation

  • Denise D. Tönissen & Joachim J. Arts & Zuo-Jun (Max) Shen, 2019. "Maintenance Location Routing for Rolling Stock Under Line and Fleet Planning Uncertainty," Transportation Science, INFORMS, vol. 53(5), pages 1252-1270, September.
    • Handle: RePEc:inm:ortrsc:v:53:y:2019:i:5:p:1252-1270
    DOI: 10.1287/trsc.2018.0866
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    References listed on IDEAS

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    Cited by:

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    2. Swapnesh Subramanian & Narayan Rangaraj, 2022. "Optimizing large scale upgradations involving movable assets," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 930-944, September.
    3. Denise D. Tönissen & Joachim J. Arts & Zuo-Jun Max Shen, 2021. "A column-and-constraint generation algorithm for two-stage stochastic programming problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 781-798, October.
    4. Tönissen, D.D. & Arts, J.J., 2020. "The stochastic maintenance location routing allocation problem for rolling stock," International Journal of Production Economics, Elsevier, vol. 230(C).
    5. Wang, Congke & Liu, Yankui & Yang, Guoqing, 2023. "Adaptive distributionally robust hub location and routing problem with a third-party logistics strategy," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    6. Martin Straka, 2021. "Design of a Computer-Aided Location Expert System Based on a Mathematical Approach," Mathematics, MDPI, vol. 9(9), pages 1-25, May.
    7. Yin, Jiateng & Pu, Fan & Yang, Lixing & D’Ariano, Andrea & Wang, Zhouhong, 2023. "Integrated optimization of rolling stock allocation and train timetables for urban rail transit networks: A benders decomposition approach," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).

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