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Performance analysis of a production system with two deteriorating machines and a finite buffer

Manufacturing systems commonly face machine degradation over time due to usage, with deteriorated machines being more likely to fail and reduce production throughput. However, accurately quantifying the impact of machine health degradation on system performance remains a challenge, which can impede...

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Bibliographic Details
Published in:Central European journal of operations research 2024-09, Vol.32 (3), p.621-651
Main Authors: Guner, Hatice Ucar, Murat, Alper, Chinnam, Ratna Babu
Format: Article
Language:English
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Summary:Manufacturing systems commonly face machine degradation over time due to usage, with deteriorated machines being more likely to fail and reduce production throughput. However, accurately quantifying the impact of machine health degradation on system performance remains a challenge, which can impede effective decision-making on the factory floor. There is a lack of analytical methods to accurately quantify the impact of machine health degradation on overall system performance, which can impede effective decision-making on the factory floor. To address this challenge, we propose an exact model for evaluating the production performance of a discrete material flow line consisting of two deteriorating machines and a finite buffer. Our model considers cases where failed machines can be perfectly or imperfectly repaired or maintained and uses Markov chains to model the state transitions of the system and estimate the throughput, which can assist in making better production and maintenance decisions. We investigate the influence of system parameters on performance, including the interactions between machine condition, system throughput, and buffer levels, and compare the performance of our method with existing approaches that consider only one operational state or use equivalent reliability parameters for all states. Our approach provides a more accurate and comprehensive analysis of the impact of machine degradation on system performance, with significant implications for improving manufacturing process efficiency and effectiveness.
ISSN:1435-246X
1613-9178
DOI:10.1007/s10100-023-00871-3