Performance evaluation for manufacturing systems under control-limit maintenance policy. (April 2020)

• Record Type: Journal Article
• Authors: Gu, Xi; Guo, Weihong; Jin, Xiaoning
• Is Part Of: Journal of manufacturing systems. Volume 55(2020)
• Page Start: 221
• Page End: 232
• Publication Date: 2020-04
• Subjects: Maintenance -- Manufacturing systems -- Performance evaluation -- Markov Chain; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.03.003
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A control-limit maintenance policy is established for machines under multi-state degradation. A decomposition method is developed to study the steady-state and transient system performance. Two models with different maintenance duration distributions are analyzed. The accuracy of the proposed method and its influencing factors are investigated. The system throughput can be improved by reducing the variance in the maintenance duration. Abstract: Maintenance decision-making in large manufacturing systems is complex as it requires the integration of various information. A control-limit policy is popular in practice, where maintenance is carried out when the degradation state of a machine reaches a threshold value. In this paper, by developing a framework based on discrete-time Markov chain models, we evaluate the system performance under the control-limit policy, in manufacturing systems that consist of multi-state machines and intermediate buffers. An exact analysis is performed for a two-machine-one-buffer system and an approximation method based on system decomposition is developed for multi-stage systems. Both steady-state and transient performance is analyzed. Numerical examples are presented to demonstrate the accuracy of the proposed method and the impact of different parameters (e.g., buffer capacity, uncertainty in the maintenance duration) on the system performance.