IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v231y2017i5p495-507.html
   My bibliography  Save this article

Optimizing the re-profiling policy regarding metropolitan train wheels based on a semi-Markov decision process

Author

Listed:
  • Zengqiang Jiang
  • Dragan Banjevic
  • Mingcheng E
  • Bing Li

Abstract

In this article, we present a maintenance model for metropolitan train wheels subjected to diameter or flange thickness overruns that includes condition monitoring with periodic inspection. We present a dynamic ( x θ , r ) policy based on condition monitoring information, where x θ is the wheel flange thickness threshold that triggers preventive re-profiling and r is the recovery value for the wheel flange thickness after preventive re-profiling. The problem is modelled as a semi-Markov decision process that considers wear in terms of the diameter and flange thickness simultaneously. The problem is formulated in a two-dimensional state space; this space is defined as a combination of the diameter state and the flange thickness state. The model also considers imperfect wheel maintenance. The model’s objective is to minimize the maintenance cost per unit time that is expected in the long run. We apply a policy-iteration algorithm as the computational approach to determine the optimal re-profiling policy and use an example to demonstrate the method’s effectiveness.

Suggested Citation

  • Zengqiang Jiang & Dragan Banjevic & Mingcheng E & Bing Li, 2017. "Optimizing the re-profiling policy regarding metropolitan train wheels based on a semi-Markov decision process," Journal of Risk and Reliability, , vol. 231(5), pages 495-507, October.
    • Handle: RePEc:sae:risrel:v:231:y:2017:i:5:p:495-507
    DOI: 10.1177/1748006X17710816
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X17710816
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X17710816?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Tang, Diyin & Makis, Viliam & Jafari, Leila & Yu, Jinsong, 2015. "Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 198-207.
    3. Suzanne Childress & Pablo Durango‐Cohen, 2005. "On parallel machine replacement problems with general replacement cost functions and stochastic deterioration," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(5), pages 409-419, August.
    4. Makis, Viliam & Jardine, Andrew K. S., 1993. "A note on optimal replacement policy under general repair," European Journal of Operational Research, Elsevier, vol. 69(1), pages 75-82, August.
    5. Love, C. E. & Zhang, Z. G. & Zitron, M. A. & Guo, R., 2000. "A discrete semi-Markov decision model to determine the optimal repair/replacement policy under general repairs," European Journal of Operational Research, Elsevier, vol. 125(2), pages 398-409, September.
    6. Wang, Ling & Xu, Hong & Yuan, Hua & Zhao, Wenjie & Chen, Xiai, 2015. "Optimizing the re-profiling strategy of metro wheels based on a data-driven wear model," European Journal of Operational Research, Elsevier, vol. 242(3), pages 975-986.
    7. Chen, Dongyan & Trivedi, Kishor S., 2005. "Optimization for condition-based maintenance with semi-Markov decision process," Reliability Engineering and System Safety, Elsevier, vol. 90(1), pages 25-29.
    Full references (including those not matched with items on IDEAS)

    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. Guo R. & Ascher H. & Love E., 2001. "Towards Practical and Synthetical Modelling of Repairable Systems," Stochastics and Quality Control, De Gruyter, vol. 16(1), pages 147-182, January.
    2. Dehayem Nodem, F.I. & Kenné, J.P. & Gharbi, A., 2011. "Simultaneous control of production, repair/replacement and preventive maintenance of deteriorating manufacturing systems," International Journal of Production Economics, Elsevier, vol. 134(1), pages 271-282, November.
    3. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    4. Nguyen, Dinh Tuan & Dijoux, Yann & Fouladirad, Mitra, 2017. "Analytical properties of an imperfect repair model and application in preventive maintenance scheduling," European Journal of Operational Research, Elsevier, vol. 256(2), pages 439-453.
    5. 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.
    6. Zhang, Xueqing & Gao, Hui, 2012. "Road maintenance optimization through a discrete-time semi-Markov decision process," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 110-119.
    7. Sayyideh Mehri Mousavi & Hesam Shams & Shahrzad Ahmadi, 2017. "Simultaneous optimization of repair and control-limit policy in condition-based maintenance," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 245-254, January.
    8. Dimitrakos, T.D. & Kyriakidis, E.G., 2007. "An improved algorithm for the computation of the optimal repair/replacement policy under general repairs," European Journal of Operational Research, Elsevier, vol. 182(2), pages 775-782, October.
    9. Dehayem Nodem, F.I. & Kenne, J.P. & Gharbi, A., 2009. "Hierarchical decision making in production and repair/replacement planning with imperfect repairs under uncertainties," European Journal of Operational Research, Elsevier, vol. 198(1), pages 173-189, October.
    10. Marais, Karen B., 2013. "Value maximizing maintenance policies under general repair," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 76-87.
    11. Liu, Xingheng & Finkelstein, Maxim & Vatn, Jørn & Dijoux, Yann, 2020. "Steady-state imperfect repair models," European Journal of Operational Research, Elsevier, vol. 286(2), pages 538-546.
    12. Ricardo José Ferreira & Paulo Renato Alves Firmino & Cláudio Tadeu Cristino, 2015. "A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.
    13. J Ansell & T Archibald & J Dagpunar & L Thomas & P Abell & D Duncalf, 2003. "Analysing maintenance data to gain insight into systems performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(4), pages 343-349, April.
    14. Love, C. E. & Zhang, Z. G. & Zitron, M. A. & Guo, R., 2000. "A discrete semi-Markov decision model to determine the optimal repair/replacement policy under general repairs," European Journal of Operational Research, Elsevier, vol. 125(2), pages 398-409, September.
    15. Yaping Li & Enrico Zio & Ershun Pan, 2021. "An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction," Journal of Risk and Reliability, , vol. 235(5), pages 831-844, October.
    16. Dimitrakos, T.D. & Kyriakidis, E.G., 2008. "A semi-Markov decision algorithm for the maintenance of a production system with buffer capacity and continuous repair times," International Journal of Production Economics, Elsevier, vol. 111(2), pages 752-762, February.
    17. 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.
    18. 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).
    19. Zhao, Yunfei & Huang, Linan & Smidts, Carol & Zhu, Quanyan, 2020. "Finite-horizon semi-Markov game for time-sensitive attack response and probabilistic risk assessment in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    20. Zhou, Zhi-Jie & Hu, Chang-Hua & Xu, Dong-Ling & Chen, Mao-Yin & Zhou, Dong-Hua, 2010. "A model for real-time failure prognosis based on hidden Markov model and belief rule base," European Journal of Operational Research, Elsevier, vol. 207(1), pages 269-283, November.

    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:sae:risrel:v:231:y:2017:i:5:p:495-507. 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: SAGE Publications (email available below). General contact details of provider: .

    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.