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Performance indicator-based multi-objective reliability optimization for multi-type production systems with heterogeneous machines

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  • Hsieh, Tsung-Jung

Abstract

The demand for product manufacturing is gradually leading the direction of production. Therefore, a production line that can produce diversified products is critical for meeting the requirements of modern systems. This study investigates multi-objective optimization of the reliability of a multi-type production system. In particular, machine deployment at each workstation can be associated with different reliability depending on the purchasing year or brand. Consequently, this study applies a component mixing strategy to multi-type production systems to estimate the reliability of an ensemble of heterogeneous machines. The goal of the studied problem is to maximize all production lines, and the cold-standby redundancy strategy based on the continuous-time Markov chain model is employed to calculate the exact reliability of the system. A performance indicator-based multi-objective algorithm, ε-MOABC, was employed to search for a near-optimal solution. The experimental results show that the multi-objective solution effectively converges to the Pareto front of the knee region and, more importantly, it can achieve high system reliability at a streamlined cost.

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  • Hsieh, Tsung-Jung, 2023. "Performance indicator-based multi-objective reliability optimization for multi-type production systems with heterogeneous machines," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005853
    DOI: 10.1016/j.ress.2022.108970
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    References listed on IDEAS

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    1. Hsieh, Tsung-Jung, 2023. "A Q-learning guided search for developing a hybrid of mixed redundancy strategies to improve system reliability," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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