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Reliability estimation for a stochastic production system with finite buffer storage by a simulation approach

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  • Ping-Chen Chang

    (National Quemoy University)

Abstract

This study develops a novel Monte Carlo simulation (MCS) approach to estimate system reliability for a stochastic production system with finite buffer storage. System reliability indicates the probability of all workstations providing sufficient capacities to satisfy a specified demand, as well as that all buffer stations are not running out of storage. First, buffer stations are modeled in a stochastic production network (SPN) model and their storage usage is analyzed based on the network-structured SPN. Second, an MCS is developed to generate the system state and to check the storage usage of buffer stations to determine whether the demand can be satisfied. After repeated simulations, the system reliability of the SPN can be estimated. Experimental results show that the proposed MCS approach is effective and efficient in estimating system reliability with reasonable quality for an SPN within a reasonable time. More importantly, system reliability will be overestimated with infinite buffer storage, and thus, it is worth studying finite buffer storage.

Suggested Citation

  • Ping-Chen Chang, 2019. "Reliability estimation for a stochastic production system with finite buffer storage by a simulation approach," Annals of Operations Research, Springer, vol. 277(1), pages 119-133, June.
    • Handle: RePEc:spr:annopr:v:277:y:2019:i:1:d:10.1007_s10479-017-2580-6
    DOI: 10.1007/s10479-017-2580-6
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    Cited by:

    1. Chang, Ping-Chen, 2022. "MC-based simulation approach for two-terminal multi-state network reliability evaluation without knowing d-MCs," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Zhang, Yongjin & Zhao, Ming & Zhang, Yanjun & Pan, Ruilin & Cai, Jing, 2020. "Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers," European Journal of Operational Research, Elsevier, vol. 283(2), pages 491-510.
    3. Yarong Chen & Hongming Zhou & Peiyu Huang & FuhDer Chou & Shenquan Huang, 2022. "A refined order release method for achieving robustness of non-repetitive dynamic manufacturing system performance," Annals of Operations Research, Springer, vol. 311(1), pages 65-79, April.
    4. Chang, Ping-Chen & Huang, Ding-Hsiang & Lin, Yi-Kuei & Nguyen, Thi-Phuong, 2021. "Reliability and maintenance models for a time-related multi-state flow network via d-MC approach," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2022. "Minimizing mission cost for production system with unreliable storage," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    6. Ping-Chen Chang, 2022. "Reliability evaluation and big data analytics architecture for a stochastic flow network with time attribute," Annals of Operations Research, Springer, vol. 311(1), pages 3-18, April.
    7. Yi-Kuei Lin & Lance Fiondella & Ping-Chen Chang, 2022. "Reliability of time-constrained multi-state network susceptible to correlated component faults," Annals of Operations Research, Springer, vol. 311(1), pages 239-254, April.
    8. Yifan Zhou & Chao Yuan & Tian Ran Lin & Lin Ma, 2021. "Maintenance policy structure investigation and optimisation of a complex production system with intermediate buffers," Journal of Risk and Reliability, , vol. 235(3), pages 458-473, June.

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