Stochastic Models in Probability and Statistics, Volume (1), No (2), Year (2024-12) , Pages (153-169)

Title : ( Some copula-based results on optimal number of cold standby components in parallel systems )

Authors: Motahareh Parsa , Hadi Jabbari Nooghabi , Jafar Ahmadi ,

Citation: BibTeX | EndNote

Abstract

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.

Keywords

Cold standby redundancy ‎ ‎Copula function Profit function ‎ ‎Reliability ‎System efficiency
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@article{paperid:1100324,
author = {Parsa, Motahareh and Jabbari Nooghabi, Hadi and Ahmadi, Jafar},
title = {Some copula-based results on optimal number of cold standby components in parallel systems},
journal = {Stochastic Models in Probability and Statistics},
year = {2024},
volume = {1},
number = {2},
month = {December},
issn = {3060-7876},
pages = {153--169},
numpages = {16},
keywords = {Cold standby redundancy ‎ ‎Copula function Profit function ‎ ‎Reliability ‎System efficiency},
}

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%0 Journal Article
%T Some copula-based results on optimal number of cold standby components in parallel systems
%A Parsa, Motahareh
%A Jabbari Nooghabi, Hadi
%A Ahmadi, Jafar
%J Stochastic Models in Probability and Statistics
%@ 3060-7876
%D 2024

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