This paper deals with a system's profit and efficiency functions consisting of a two-parallel components supported by (n-2) cold standby redundancy. It is supposed that the components are not repairable and the failed component is immediately replaced with one of the cold standby components. Because the lifetimes of active components are dependent, their dependency is modelled by Farlie-Gumbel-Morgenstern (FGM) copula function. Finding the optimal $n$ according to the highest profit function of the system and efficiency values is studied while the effect of the dependence parameter is considered. It is concluded that though increasing n grants higher system reliability it is not always wise as long as the redundancy maintenance costs. Due to the complexity of the formulas, for the large values of n, the results are analyzed numerically and graphically. Finally, some examples are given to illustrate the results.
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Parsa, M. , Jabbari, H. and Ahmadi, J. (2024). Some copula-based results on optimal number of cold standby components in parallel systems. Stochastic Models in Probability and Statistics, 1(2), 153-169. doi: 10.22067/smps.2024.45746
MLA
Parsa, M. , , Jabbari, H. , and Ahmadi, J. . "Some copula-based results on optimal number of cold standby components in parallel systems", Stochastic Models in Probability and Statistics, 1, 2, 2024, 153-169. doi: 10.22067/smps.2024.45746
HARVARD
Parsa, M., Jabbari, H., Ahmadi, J. (2024). 'Some copula-based results on optimal number of cold standby components in parallel systems', Stochastic Models in Probability and Statistics, 1(2), pp. 153-169. doi: 10.22067/smps.2024.45746
CHICAGO
M. Parsa , H. Jabbari and J. Ahmadi, "Some copula-based results on optimal number of cold standby components in parallel systems," Stochastic Models in Probability and Statistics, 1 2 (2024): 153-169, doi: 10.22067/smps.2024.45746
VANCOUVER
Parsa, M., Jabbari, H., Ahmadi, J. Some copula-based results on optimal number of cold standby components in parallel systems. Stochastic Models in Probability and Statistics, 2024; 1(2): 153-169. doi: 10.22067/smps.2024.45746
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