IDEAS home Printed from https://ideas.repec.org/a/inm/ortrsc/v28y1994i1p55-62.html
   My bibliography  Save this article

Optimal Inspection and Repair Policies for Infrastructure Facilities

Author

Listed:
  • Samer Madanat

    (School of Civil Engineering, Purdue University, West Lafayette, Indiana 47907)

  • Moshe Ben-Akiva

    (Civil Engineering Department, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

State-of-the-art decision-making models in the area of infrastructure maintenance and rehabilitation (which are based on the Markov Decision Process) do not take into account the uncertainty in the measurement of facility condition. This paper presents a methodology, the Latent Markov Decision Process (LMDP), which explicitly recognizes the presence of random errors in the measurement of the condition of infrastructure facilities. Two versions of the LMDP are presented. In the first version, the inspection schedule is fixed, which is the usual assumption made in state-of-the-art models. The second version of the LMDP minimizes the sum of inspection and M & R costs. An empirical comparison of the two versions of the LMDP and the traditional MDP illustrates the importance of incorporating measurement uncertainty in decision-making and of optimizing the inspection schedule.

Suggested Citation

  • Samer Madanat & Moshe Ben-Akiva, 1994. "Optimal Inspection and Repair Policies for Infrastructure Facilities," Transportation Science, INFORMS, vol. 28(1), pages 55-62, February.
    • Handle: RePEc:inm:ortrsc:v:28:y:1994:i:1:p:55-62
    DOI: 10.1287/trsc.28.1.55
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/trsc.28.1.55
    Download Restriction: no
    File URL: https://libkey.io/10.1287/trsc.28.1.55?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
    ---><---

    Citations

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


    Cited by:

    1. Zhi-Chun Li & Dian Sheng, 2014. "Pavement rehabilitation scheduling and toll pricing under different regulatory regimes," Annals of Operations Research, Springer, vol. 217(1), pages 337-355, June.
    2. Joaquim AP Braga & António R Andrade, 2019. "Optimizing maintenance decisions in railway wheelsets: A Markov decision process approach," Journal of Risk and Reliability, , vol. 233(2), pages 285-300, April.
    3. Özgür-Ünlüakın, Demet & Bilgiç, Taner, 2017. "Performance analysis of an aggregation and disaggregation solution procedure to obtain a maintenance plan for a partially observable multi-component system," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 652-662.
    4. Durango, Pablo L. & Madanat, Samer M., 2002. "Optimal maintenance and repair policies in infrastructure management under uncertain facility deterioration rates: an adaptive control approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(9), pages 763-778, November.
    5. Kıvanç, İpek & Özgür-Ünlüakın, Demet & Bilgiç, Taner, 2022. "Maintenance policy analysis of the regenerative air heater system using factored POMDPs," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. Kuhn, Kenneth D. & Madanat, Samer M., 2005. "Robust Maintenance Policies for Markovian Systems under Model Uncertainty," University of California Transportation Center, Working Papers qt1d85j6mt, University of California Transportation Center.
    7. Papakonstantinou, K.G. & Shinozuka, M., 2014. "Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 214-224.
    8. Rafic Faddoul & Abdul-Hamid Soubra & Wassim Raphael & Alaa Chateauneuf, 2013. "Extension of dynamic programming models for management optimization from single structure to multi-structures level," Post-Print hal-01006860, HAL.
    9. Durango-Cohen, Pablo L. & Madanat, Samer M., 2008. "Optimization of inspection and maintenance decisions for infrastructure facilities under performance model uncertainty: A quasi-Bayes approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(8), pages 1074-1085, October.
    10. Madanat, S M & Durango, Pablo L, 2001. "Optimal maintenance and repair policies in infrastructure management under uncertain facility deterioration rates: an adaptive control approach," University of California Transportation Center, Working Papers qt8jz8h9fw, University of California Transportation Center.
    11. Mishalani, Rabi G. & Gong, Liying, 2009. "Optimal infrastructure condition sampling over space and time for maintenance decision-making under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 43(3), pages 311-324, March.
    12. Zhang, Le & Fu, Liangliang & Gu, Weihua & Ouyang, Yanfeng & Hu, Yaohua, 2017. "A general iterative approach for the system-level joint optimization of pavement maintenance, rehabilitation, and reconstruction planning," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 378-400.
    13. Pantha, Bhoj Raj & Yatabe, Ryuichi & Bhandary, Netra Prakash, 2010. "GIS-based highway maintenance prioritization model: an integrated approach for highway maintenance in Nepal mountains," Journal of Transport Geography, Elsevier, vol. 18(3), pages 426-433.
    14. 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.
    15. Lee, Jinwoo & Madanat, Samer, 2015. "A joint bottom-up solution methodology for system-level pavement rehabilitation and reconstruction," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 106-122.
    16. Seites-Rundlett, William & Bashar, Mohammad Z. & Torres-Machi, Cristina & Corotis, Ross B., 2022. "Combined evidence model to enhance pavement condition prediction from highly uncertain sensor data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    17. Yingnan Yang & Hongming Xie, 2021. "Determination of Optimal MR&R Strategy and Inspection Intervals to Support Infrastructure Maintenance Decision Making," Sustainability, MDPI, vol. 13(5), pages 1-10, March.
    18. Papakonstantinou, K.G. & Shinozuka, M., 2014. "Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 202-213.
    19. Durango-Cohen, Pablo L., 2007. "A time series analysis framework for transportation infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 493-505, June.
    20. Kobayashi, Kiyoshi & Kaito, Kiyoyuki & Lethanh, Nam, 2012. "A statistical deterioration forecasting method using hidden Markov model for infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 544-561.

    More about this item

    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:inm:ortrsc:v:28:y:1994:i:1:p:55-62. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.