IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v19y2019i2d10.1007_s12351-017-0296-7.html
   My bibliography  Save this article

The improvement upon the reliability of the k-out-of-n:F system with the repair rates differentiation policy

Author

Listed:
  • Xiaojun Liang

    (Sichuan Normal University)

  • Yinghui Tang

    (Sichuan Normal University
    Sichuan Normal University)

Abstract

A manufacturing system includes not only the production system but also a repair facility. With increasing number of repairs, the repaired components will become more fragile and need more time to repair when they fail. In such case, the repair facility might suffer a “congestion” dilemma if the failed components cannot be repaired immediately. This paper investigates the reliability improvement of a k-out-of-n:F system under repair-rate differentiation policy, which might decrease the expected waiting time for repair without an increase in repair capacity. First, by comparing the mean queue-length in front of the repair facility, we show that the mixed-repair policy developed in this paper is dominant over the pure-repair policy in relieving the repair congestion problem. Then, we derive the optimal solution under the repair-rate differentiation policy, which minimizes the expected waiting time for repair of the failed components. Finally, when the buffer area is exhausted or component deterioration becomes too severe, the time of batch replacement is proposed in a practical example of the textile mill.

Suggested Citation

  • Xiaojun Liang & Yinghui Tang, 2019. "The improvement upon the reliability of the k-out-of-n:F system with the repair rates differentiation policy," Operational Research, Springer, vol. 19(2), pages 479-500, June.
    • Handle: RePEc:spr:operea:v:19:y:2019:i:2:d:10.1007_s12351-017-0296-7
    DOI: 10.1007/s12351-017-0296-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-017-0296-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-017-0296-7?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. Baric{s} Ata & Shiri Shneorson, 2006. "Dynamic Control of an M/M/1 Service System with Adjustable Arrival and Service Rates," Management Science, INFORMS, vol. 52(11), pages 1778-1791, November.
    2. Wallace J. Hopp & Seyed M. R. Iravani & Gigi Y. Yuen, 2007. "Operations Systems with Discretionary Task Completion," Management Science, INFORMS, vol. 53(1), pages 61-77, January.
    3. Eryilmaz, Serkan, 2012. "On the mean residual life of a k-out-of-n:G system with a single cold standby component," European Journal of Operational Research, Elsevier, vol. 222(2), pages 273-277.
    4. Vasiliki Kostami & Sampath Rajagopalan, 2014. "Speed–Quality Trade-Offs in a Dynamic Model," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 104-118, February.
    5. Yamamoto, Hisashi & Akiba, Tomoaki & Nagatsuka, Hideki & Moriyama, Yurie, 2008. "Recursive algorithm for the reliability of a connected-(1, 2)-or-(2, 1)-out-of-(m, n):F lattice system," European Journal of Operational Research, Elsevier, vol. 188(3), pages 854-864, August.
    6. Haim Mendelson & Seungjin Whang, 1990. "Optimal Incentive-Compatible Priority Pricing for the M/M/1 Queue," Operations Research, INFORMS, vol. 38(5), pages 870-883, October.
    7. Xiang, Yanping & Levitin, Gregory, 2012. "Combined m-consecutive and k-out-of-n sliding window systems," European Journal of Operational Research, Elsevier, vol. 219(1), pages 105-113.
    8. Jennifer M. George & J. Michael Harrison, 2001. "Dynamic Control of a Queue with Adjustable Service Rate," Operations Research, INFORMS, vol. 49(5), pages 720-731, October.
    9. S. Rao & E. R. Petersen, 1998. "Optimal Pricing of Priority Services," Operations Research, INFORMS, vol. 46(1), pages 46-56, February.
    10. Wang, Yong & Li, Lin & Huang, Shuhong & Chang, Qing, 2012. "Reliability and covariance estimation of weighted k-out-of-n multi-state systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 138-147.
    11. Itai Gurvich & Ward Whitt, 2010. "Service-Level Differentiation in Many-Server Service Systems via Queue-Ratio Routing," Operations Research, INFORMS, vol. 58(2), pages 316-328, April.
    12. Mor Armony & Avishai Mandelbaum, 2011. "Routing and Staffing in Large-Scale Service Systems: The Case of Homogeneous Impatient Customers and Heterogeneous Servers," Operations Research, INFORMS, vol. 59(1), pages 50-65, February.
    13. Jan A. Van Mieghem, 2000. "Price and Service Discrimination in Queuing Systems: Incentive Compatibility of Gc\mu Scheduling," Management Science, INFORMS, vol. 46(9), pages 1249-1267, September.
    14. Itay Gurvich & Mor Armony & Avishai Mandelbaum, 2008. "Service-Level Differentiation in Call Centers with Fully Flexible Servers," Management Science, INFORMS, vol. 54(2), pages 279-294, February.
    15. Xie, Wei & Liao, Haitao & Jin, Tongdan, 2014. "Maximizing system availability through joint decision on component redundancy and spares inventory," European Journal of Operational Research, Elsevier, vol. 237(1), pages 164-176.
    16. Ying Xu & Alan Scheller-Wolf & Katia Sycara, 2015. "The Benefit of Introducing Variability in Single-Server Queues with Application to Quality-Based Service Domains," Operations Research, INFORMS, vol. 63(1), pages 233-246, February.
    17. Fernández, Arturo J., 2015. "Optimum attributes component test plans for k-out-of-n:F Weibull systems using prior information," European Journal of Operational Research, Elsevier, vol. 240(3), pages 688-696.
    18. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2014. "Cold vs. hot standby mission operation cost minimization for 1-out-of-N systems," European Journal of Operational Research, Elsevier, vol. 234(1), pages 155-162.
    19. Wells, Charles E., 2014. "Reliability analysis of a single warm-standby system subject to repairable and nonrepairable failures," European Journal of Operational Research, Elsevier, vol. 235(1), pages 180-186.
    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. Ying Xu & Alan Scheller-Wolf & Katia Sycara, 2015. "The Benefit of Introducing Variability in Single-Server Queues with Application to Quality-Based Service Domains," Operations Research, INFORMS, vol. 63(1), pages 233-246, February.
    2. Fernández, Arturo J., 2015. "Optimum attributes component test plans for k-out-of-n:F Weibull systems using prior information," European Journal of Operational Research, Elsevier, vol. 240(3), pages 688-696.
    3. Nizar Mannai & Soufiane Gasmi, 2020. "Optimal design of k-out-of-n system under first and last replacement in reliability theory," Operational Research, Springer, vol. 20(3), pages 1353-1368, September.
    4. Jinting Wang & Zhongbin Wang & Yunan Liu, 2020. "Reducing Delay in Retrial Queues by Simultaneously Differentiating Service and Retrial Rates," Operations Research, INFORMS, vol. 68(6), pages 1648-1667, November.
    5. Noa Zychlinski, 2023. "Applications of fluid models in service operations management," Queueing Systems: Theory and Applications, Springer, vol. 103(1), pages 161-185, February.
    6. Philipp Afèche, 2013. "Incentive-Compatible Revenue Management in Queueing Systems: Optimal Strategic Delay," Manufacturing & Service Operations Management, INFORMS, vol. 15(3), pages 423-443, July.
    7. Barιş Ata & Deishin Lee & Erkut Sönmez, 2019. "Dynamic Volunteer Staffing in Multicrop Gleaning Operations," Operations Research, INFORMS, vol. 67(2), pages 295-314, March.
    8. Erica L. Plambeck, 2004. "Optimal Leadtime Differentiation via Diffusion Approximations," Operations Research, INFORMS, vol. 52(2), pages 213-228, April.
    9. Chen-An Lin & Kevin Shang & Peng Sun, 2023. "Wait Time–Based Pricing for Queues with Customer-Chosen Service Times," Management Science, INFORMS, vol. 69(4), pages 2127-2146, April.
    10. Sami Najafi-Asadolahi & Kristin Fridgeirsdottir, 2014. "Cost-per-Click Pricing for Display Advertising," Manufacturing & Service Operations Management, INFORMS, vol. 16(4), pages 482-497, October.
    11. Zhang, Nan & Fouladirad, Mitra & Barros, Anne, 2019. "Reliability-based measures and prognostic analysis of a K-out-of-N system in a random environment," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1120-1131.
    12. Vasiliki Kostami, 2020. "Price and Lead time Disclosure Strategies in Inventory Systems," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2760-2788, December.
    13. Roy Cerqueti, 2022. "A new concept of reliability system and applications in finance," Annals of Operations Research, Springer, vol. 312(1), pages 45-64, May.
    14. Tianhu Deng & Ying‐Ju Chen & Zuo‐Jun Max Shen, 2015. "Optimal pricing and scheduling control of product shipping," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(3), pages 215-227, April.
    15. Seung Bum Soh & Itai Gurvich, 2017. "Call Center Staffing: Service-Level Constraints and Index Priorities," Operations Research, INFORMS, vol. 65(2), pages 537-555, April.
    16. René Caldentey & Lawrence M. Wein, 2006. "Revenue Management of a Make-to-Stock Queue," Operations Research, INFORMS, vol. 54(5), pages 859-875, October.
    17. Philipp Afèche & J. Michael Pavlin, 2016. "Optimal Price/Lead-Time Menus for Queues with Customer Choice: Segmentation, Pooling, and Strategic Delay," Management Science, INFORMS, vol. 62(8), pages 2412-2436, August.
    18. Philipp Afèche & Opher Baron & Joseph Milner & Ricky Roet-Green, 2019. "Pricing and Prioritizing Time-Sensitive Customers with Heterogeneous Demand Rates," Operations Research, INFORMS, vol. 67(4), pages 1184-1208, July.
    19. Amy R. Ward & Mor Armony, 2013. "Blind Fair Routing in Large-Scale Service Systems with Heterogeneous Customers and Servers," Operations Research, INFORMS, vol. 61(1), pages 228-243, February.
    20. Shone, Rob & Glazebrook, Kevin & Zografos, Konstantinos G., 2019. "Resource allocation in congested queueing systems with time-varying demand: An application to airport operations," European Journal of Operational Research, Elsevier, vol. 276(2), pages 566-581.

    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:spr:operea:v:19:y:2019:i:2:d:10.1007_s12351-017-0296-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.