IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v269y2018i1p341-352.html
   My bibliography  Save this article

A hybrid Constraint Programming/Mixed Integer Programming framework for the preventive signaling maintenance crew scheduling problem

Author

Listed:
  • M. Pour, Shahrzad
  • Drake, John H.
  • Ejlertsen, Lena Secher
  • Rasmussen, Kourosh Marjani
  • Burke, Edmund K.

Abstract

A railway signaling system is a complex and interdependent system which should ensure the safe operation of trains. We introduce and address a mixed integer optimisation model for the preventive signal maintenance crew scheduling problem in the Danish railway system. The problem contains many practical constraints, such as temporal dependencies between crew schedules, the splitting of tasks across multiple days, crew competency requirements and several other managerial constraints. We propose a novel hybrid framework using Constraint Programming to generate initial feasible solutions to feed as ‘warm start’ solutions to a Mixed Integer Programming solver for further improvement. We apply this hybrid framework to a section of the Danish rail network and benchmark our results against both direct application of a Mixed Integer Programming solver and modelling the problem as a Constraint Optimisation Problem. Whereas the current practice of using a general purpose Mixed Integer Programming solver is only able to solve instances over a two-week planning horizon, the hybrid framework generates good results for problem instances over an eight-week period. In addition, the use of a Mixed Integer Programming solver to improve the initial solutions generated by Constraint Programming is shown to be significantly superior to addressing the problem as a Constraint Optimisation Problem.

Suggested Citation

  • M. Pour, Shahrzad & Drake, John H. & Ejlertsen, Lena Secher & Rasmussen, Kourosh Marjani & Burke, Edmund K., 2018. "A hybrid Constraint Programming/Mixed Integer Programming framework for the preventive signaling maintenance crew scheduling problem," European Journal of Operational Research, Elsevier, vol. 269(1), pages 341-352.
    • Handle: RePEc:eee:ejores:v:269:y:2018:i:1:p:341-352
    DOI: 10.1016/j.ejor.2017.08.033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221717307646
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2017.08.033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Ashish K. Nemani & Suat Bog & Ravindra K. Ahuja, 2010. "Solving the Curfew Planning Problem," Transportation Science, INFORMS, vol. 44(4), pages 506-523, November.
    3. S Boğ & A K Nemani & R K Ahuja, 2011. "Iterative algorithms for the curfew planning problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 593-607, April.
    4. Wen, M. & Li, R. & Salling, K.B., 2016. "Optimization of preventive condition-based tamping for railway tracks," European Journal of Operational Research, Elsevier, vol. 252(2), pages 455-465.
    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. Aarabi, Fatemeh & Batta, Rajan, 2020. "Scheduling spatially distributed jobs with degradation: Application to pothole repair," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    2. Wang, Jiaxi, 2024. "Maintenance scheduling at high-speed train depots: An optimization approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Fei Peng & Xian Fan & Puxin Wang & Mingan Sheng, 2022. "A Time-Space Network-Based Optimization Method for Scheduling Depot Drivers," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    4. Bahman Naderi & Rubén Ruiz & Vahid Roshanaei, 2023. "Mixed-Integer Programming vs. Constraint Programming for Shop Scheduling Problems: New Results and Outlook," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 817-843, July.

    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. 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).
    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. Alice Consilvio & Angela Febbraro & Rossella Meo & Nicola Sacco, 2019. "Risk-based optimal scheduling of maintenance activities in a railway network," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 435-465, December.
    4. Zhang, Huimin & Li, Shukai & Wang, Yihui & Yang, Lixing & Gao, Ziyou, 2021. "Collaborative real-time optimization strategy for train rescheduling and track emergency maintenance of high-speed railway: A Lagrangian relaxation-based decomposition algorithm," Omega, Elsevier, vol. 102(C).
    5. Tolou Esfandeh & Rajan Batta & Changhyun Kwon, 2018. "Time-Dependent Hazardous-Materials Network Design Problem," Transportation Science, INFORMS, vol. 52(2), pages 454-473, March.
    6. Alice Consilvio & José Solís-Hernández & Noemi Jiménez-Redondo & Paolo Sanetti & Federico Papa & Iñigo Mingolarra-Garaizar, 2020. "On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies," Sustainability, MDPI, vol. 12(6), pages 1-24, March.
    7. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    8. Wang, Jiaxi, 2024. "Maintenance scheduling at high-speed train depots: An optimization approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Tomas Lidén, 2020. "Coordinating maintenance windows and train traffic: a case study," Public Transport, Springer, vol. 12(2), pages 261-298, June.
    10. 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.

    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:eee:ejores:v:269:y:2018:i:1:p:341-352. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.