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Optimal replacement policy for multi-state manufacturing system with economic and resource dependence under epistemic uncertainty.

Authors :
Chen, Zhaoxiang
Chen, Zhen
Zhou, Di
Shao, Chi
Pan, Ershun
Source :
International Journal of Production Research; Oct2023, Vol. 61 Issue 20, p6772-6790, 19p, 1 Color Photograph, 3 Diagrams, 9 Charts, 2 Graphs
Publication Year :
2023

Abstract

This paper develops an optimal replacement policy V* for a multi-state manufacturing system. The manufacturing system would be repaired imperfectly once its performance cannot meet the production demand, and would be replaced when the production demand is not met for the V*-th time. Due to imprecise state assignments and unpredictable external working conditions, the performance and transition intensity of the multi-state machine cannot be accurately identified and then inevitably lead to epistemic uncertainty. In addition, the economic dependence and resource dependence that prevailed in the manufacturing system should be considered. In this paper, economic dependence is described as the time and cost saved by simultaneously repairing multiple identical machines, and resource dependence is caused by finite capacity buffers. To take these into account, the fuzzy Markov model and fuzzy stochastic flow manufacturing network (FSFMN) are tailored to evaluate the fuzzy reliability of machines and manufacturing systems, respectively. To obtain the optimal replacement policy V*, we derive the expression of the long run fuzzy profit rate under epistemic uncertainty. The replacement policy is demonstrated on the ferrite phase shifting unit manufacturing system, and the results of the subsequent comparative study and sensitivity analysis show that this policy is more effective. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00207543
Volume :
61
Issue :
20
Database :
Complementary Index
Journal :
International Journal of Production Research
Publication Type :
Academic Journal
Accession number :
170023601
Full Text :
https://doi.org/10.1080/00207543.2022.2137595