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

Author

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  • 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
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    1. 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.
    2. 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.
    3. S. Rao & E. R. Petersen, 1998. "Optimal Pricing of Priority Services," Operations Research, INFORMS, vol. 46(1), pages 46-56, February.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
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