IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v230y2016i1p34-43.html
   My bibliography  Save this article

Optimal control limit policy for age-dependent deteriorating systems under incomplete observations

Author

Listed:
  • Lu Jin
  • Undarmaa Bayarsaikhan
  • Kazuyuki Suzuki

Abstract

The focus of this study is the optimal decision making problem for an age-dependent deteriorating system in which the deterioration unfolds as a non-stationary Markov process. The true deterioration state of the system cannot be known directly and is assumed to be observed incompletely by a monitor that provides information related to the true deterioration state stochastically. The optimal decision making problem is formulated as a partially observable Markov decision process. The optimal maintenance policy is investigated and the structural properties of the resulting optimal expected cost function are obtained. These structural properties establish the existence of an optimal control limit policy with respect to both the deterioration information vector and the age of the system under intuitively meaningful assumptions. The monotonic property of the control limits is also clarified.

Suggested Citation

  • Lu Jin & Undarmaa Bayarsaikhan & Kazuyuki Suzuki, 2016. "Optimal control limit policy for age-dependent deteriorating systems under incomplete observations," Journal of Risk and Reliability, , vol. 230(1), pages 34-43, February.
    • Handle: RePEc:sae:risrel:v:230:y:2016:i:1:p:34-43
    DOI: 10.1177/1748006X15589208
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X15589208
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X15589208?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. Richard D. Smallwood & Edward J. Sondik, 1973. "The Optimal Control of Partially Observable Markov Processes over a Finite Horizon," Operations Research, INFORMS, vol. 21(5), pages 1071-1088, October.
    2. Ohnishi, Masamitsu & Kawai, Hajime & Mine, Hisashi, 1986. "An optimal inspection and replacement policy under incomplete state information," European Journal of Operational Research, Elsevier, vol. 27(1), pages 117-128, October.
    3. George E. Monahan, 1982. "State of the Art---A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms," Management Science, INFORMS, vol. 28(1), pages 1-16, January.
    4. William S. Lovejoy, 1987. "Some Monotonicity Results for Partially Observed Markov Decision Processes," Operations Research, INFORMS, vol. 35(5), pages 736-743, October.
    5. Alaa H. Elwany & Nagi Z. Gebraeel & Lisa M. Maillart, 2011. "Structured Replacement Policies for Components with Complex Degradation Processes and Dedicated Sensors," Operations Research, INFORMS, vol. 59(3), pages 684-695, June.
    6. Jagpreet Chhatwal & Oguzhan Alagoz & Elizabeth S. Burnside, 2010. "Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors," Operations Research, INFORMS, vol. 58(6), pages 1577-1591, December.
    7. Grosfeld-Nir, Abraham, 2007. "Control limits for two-state partially observable Markov decision processes," European Journal of Operational Research, Elsevier, vol. 182(1), pages 300-304, October.
    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. Chiel van Oosterom & Lisa M. Maillart & Jeffrey P. Kharoufeh, 2017. "Optimal maintenance policies for a safety‐critical system and its deteriorating sensor," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(5), pages 399-417, August.
    2. Hao Zhang, 2010. "Partially Observable Markov Decision Processes: A Geometric Technique and Analysis," Operations Research, INFORMS, vol. 58(1), pages 214-228, February.
    3. Hao Zhang & Weihua Zhang, 2023. "Analytical Solution to a Partially Observable Machine Maintenance Problem with Obvious Failures," Management Science, INFORMS, vol. 69(7), pages 3993-4015, July.
    4. Malek Ebadi & Raha Akhavan-Tabatabaei, 2021. "Personalized Cotesting Policies for Cervical Cancer Screening: A POMDP Approach," Mathematics, MDPI, vol. 9(6), pages 1-20, March.
    5. Junbo Son & Yeongin Kim & Shiyu Zhou, 2022. "Alerting patients via health information system considering trust-dependent patient adherence," Information Technology and Management, Springer, vol. 23(4), pages 245-269, December.
    6. Chernonog, Tatyana & Avinadav, Tal, 2016. "A two-state partially observable Markov decision process with three actionsAuthor-Name: Ben-Zvi, Tal," European Journal of Operational Research, Elsevier, vol. 254(3), pages 957-967.
    7. Saghafian, Soroush, 2018. "Ambiguous partially observable Markov decision processes: Structural results and applications," Journal of Economic Theory, Elsevier, vol. 178(C), pages 1-35.
    8. Deep, Akash & Zhou, Shiyu & Veeramani, Dharmaraj & Chen, Yong, 2023. "Partially observable Markov decision process-based optimal maintenance planning with time-dependent observations," European Journal of Operational Research, Elsevier, vol. 311(2), pages 533-544.
    9. M. Reza Skandari & Steven M. Shechter, 2021. "Patient-Type Bayes-Adaptive Treatment Plans," Operations Research, INFORMS, vol. 69(2), pages 574-598, March.
    10. Jingyu Zhang & Brian T. Denton & Hari Balasubramanian & Nilay D. Shah & Brant A. Inman, 2012. "Optimization of Prostate Biopsy Referral Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 529-547, October.
    11. Jue Wang & Chi-Guhn Lee, 2015. "Multistate Bayesian Control Chart Over a Finite Horizon," Operations Research, INFORMS, vol. 63(4), pages 949-964, August.
    12. James T. Treharne & Charles R. Sox, 2002. "Adaptive Inventory Control for Nonstationary Demand and Partial Information," Management Science, INFORMS, vol. 48(5), pages 607-624, May.
    13. Wooseung Jang & J. George Shanthikumar, 2002. "Stochastic allocation of inspection capacity to competitive processes," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(1), pages 78-94, February.
    14. Yanling Chang & Alan Erera & Chelsea White, 2015. "Value of information for a leader–follower partially observed Markov game," Annals of Operations Research, Springer, vol. 235(1), pages 129-153, December.
    15. Vikram Krishnamurthy & Bo Wahlberg, 2009. "Partially Observed Markov Decision Process Multiarmed Bandits---Structural Results," Mathematics of Operations Research, INFORMS, vol. 34(2), pages 287-302, May.
    16. Turgay Ayer & Oguzhan Alagoz & Natasha K. Stout & Elizabeth S. Burnside, 2016. "Heterogeneity in Women’s Adherence and Its Role in Optimal Breast Cancer Screening Policies," Management Science, INFORMS, vol. 62(5), pages 1339-1362, May.
    17. Otten, Maarten & Timmer, Judith & Witteveen, Annemieke, 2020. "Stratified breast cancer follow-up using a continuous state partially observable Markov decision process," European Journal of Operational Research, Elsevier, vol. 281(2), pages 464-474.
    18. Lisa M. Maillart & Ludmila Zheltova, 2007. "Structured maintenance policies on interior sample paths," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(6), pages 645-655, September.
    19. Turgay Ayer & Oguzhan Alagoz & Natasha K. Stout, 2012. "OR Forum---A POMDP Approach to Personalize Mammography Screening Decisions," Operations Research, INFORMS, vol. 60(5), pages 1019-1034, October.
    20. L M Maillart & T G Yeung & Z Gozde Icten, 2011. "Selecting test sensitivity and specificity parameters to optimally maintain a degrading system," Journal of Risk and Reliability, , vol. 225(2), pages 131-139, June.

    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:230:y:2016:i:1:p:34-43. 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.