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Determining Production Systems Performance Metrics Considering Machine Downtime

Abstract : The monitoring and control of production system performance is a continuing concern within prioritization and optimization decisions regarding manufacturing systems. There is interest on mathematical models of systems performance that facilitate a straightforward computer implementation of continuous monitoring and improvement of the performance of production systems. In this work, it is considered a multi-state system approach for modeling production manufacturing systems and for measuring its long-term performance. One operational measure used for evaluating the manufacturing systems performance is overall equipment effectiveness. Here, the focus on overall equipment effectiveness is the total stoppage time due to corrective maintenance. This study therefore set out to assess a mathematical modeling approach for evaluating decisions about cost-efficient corrective maintenance. This approach is used for computing the manufacturing system states probability distribution, since calculating the total production cost per unit (total unit cost of production plus total unit cost of maintenance). In order to evaluate the goodness of the mathematical approach, a continuous time Markov chain model and a discrete event simulation model are used, by comparing the values obtained using the three techniques. The results demonstrate the effectiveness of the universal generating function for assessing system performance metrics.
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https://hal-utt.archives-ouvertes.fr/hal-02428583
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Monday, January 6, 2020 - 9:51:03 AM
Last modification on : Monday, May 4, 2020 - 5:42:02 PM

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Gustavo Bula, Nacef Tazi, Eric Chatelet. Determining Production Systems Performance Metrics Considering Machine Downtime. IFAC-PapersOnLine, Elsevier, 2019, 52 (13), pp.1022-1027. ⟨10.1016/j.ifacol.2019.11.329⟩. ⟨hal-02428583⟩

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