Stochastic comparisons of total capacity of weighted-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.gif" display="inline" overflow="scroll"><mml:mi>k</mml:mi></mml:math>-out-of-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.gif" display="inline" overflow="scroll"><mml:mi>n</mml:mi></mml:math> systems

Rahmani, Rabi-Allah; Izadi, Muhyiddin; Khaledi, ‎Baha-Eldin

doi:10.1016/j.spl.2016.06.005

Cited by 13 publications

(3 citation statements)

References 13 publications

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“…According to Rahmani et al (2016b); , larger weight should be allocated with component with higher reliability. Then, under Assumption 3.1, the best assembly of components and weights can be achieved by setting λ 1 ≥ λ 2 ≥ .…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, it follows from Theorem 2.2 of Rahmani et al (2016b) that ψ(t; w, X μ ) ≥ st ψ(t; w, X μ π ), where μ π is a permutation of μ with a permutation vector of indexes π . Since the usual stochastic ordering implies the increasing convex ordering, it must hold that ψ(t; w, X μ ) ≥ icx ψ(t; w, X μ π ).…”

Section: Assumption 33mentioning

confidence: 99%

“…(1) The study on reliability analysis of weighted k-outof-n system has attracted many researchers' attention in the past few years. For example, Rahmani et al (2016b) discussed the influence of components lifetimes and weights on the system's total capacity under the independent case in the sense of the hazard rate ordering and the usual stochastic ordering. When it is allowed to allocate components lifetimes to the weights, they also presented the optimal allocation policy so as to maximise the system's total capacity.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic comparisons on total capacity of weighted k-out-of-n systems with heterogeneous components

Zhang

2021

Statistical Theory and Related Fields

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This paper carries out stochastic comparisons on the total capacity of weighted k-out-of-n systems with heterogeneous components. The expectation order, the increasing convex/concave order and the usual stochastic order are employed to investigate stochastic behaviours of system capacity. Sufficient conditions are established in terms of majorisation-type orders between the vectors of component lifetime distribution parameters and the vectors of weights. Some examples are also provided as illustrations.

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Section: Resultsmentioning

confidence: 99%

Section: Assumption 33mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic comparisons on total capacity of weighted k-out-of-n systems with heterogeneous components

Zhang

2021

Statistical Theory and Related Fields

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On total capacity of k‐out‐of‐n systems with random weights

Zhang

Ding

Zhao

2018

Naval Research Logistics

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In engineering applications, many reliability systems can be modeled as k-out-of-n systems with components having random weights. Before putting such kind of system into a working state, it is of great significance for a system designer to find out the optimal assembly of the random weights to the components. In this article, we investigate the performance levels of k-out-of-n systems with random weights. Optimal assembly policies are obtained by maximizing the total capacity according to different criteria, including the usual stochastic order, the increasing convex [concave] order, and the expectation order. Based on the optimal assembly strategy derived by maximizing the expected total capacity, we further investigate stochastic properties of the resulting weighted system with respect to the vector of expectations of random weights. Numerical examples are provided to highlight our theoretical findings as well. KEYWORDS expected total capacity, LWSAI, optimal assembly, random weighted k-out-of-n system, RWSAI, SAI, stochastic orders, total capacity 1 Hereafter, for convenience, we denote the k-out-of-n system with random weights by r-w-k-out-of-n system. Formally, the definition of a r-w-k-out-of-n system is recalled as follows. Let X = (X 1 , … , X n ) be the component lifetimes vector, and W = (W 1 , … , W n ) be the random weights vector with W i denoting the weight of the ith component. Let (t; k, Naval Res Logistics 2018;65:347-359 wileyonlinelibrary.com/journal/nav

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Efficient algorithm for reliability and importance measures of linear weighted-(n,f,k)and〈
Amrutkar
¹
,
Kamalja
²

2017
Computers & Industrial Engineering
16
0
2
0
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Stochastic comparisons of total capacity of weighted-k-out-of-n systems

Cited by 13 publications

References 13 publications

Stochastic comparisons on total capacity of weighted k-out-of-n systems with heterogeneous components

Stochastic comparisons on total capacity of weighted k-out-of-n systems with heterogeneous components

On total capacity of k‐out‐of‐n systems with random weights

Efficient algorithm for reliability and importance measures of linear weighted-(n,f,k)and〈
Amrutkar
¹
,
Kamalja
²

2017
Computers & Industrial Engineering
16
0
2
0
View full text Add to dashboard Cite

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Stochastic comparisons of total capacity of weighted-k-out-of-n systems

Cited by 13 publications

References 13 publications

Stochastic comparisons on total capacity of weighted k-out-of-n systems with heterogeneous components

Stochastic comparisons on total capacity of weighted k-out-of-n systems with heterogeneous components

On total capacity of k‐out‐of‐n systems with random weights

Efficient algorithm for reliability and importance measures of linear weighted-(n,f,k)and〈Amrutkar1, Kamalja2 2017Computers & Industrial Engineering16020View full textAdd to dashboardCite

Contact Info

Product

Resources

About

Efficient algorithm for reliability and importance measures of linear weighted-(n,f,k)and〈
Amrutkar
¹
,
Kamalja
²

2017
Computers & Industrial Engineering
16
0
2
0
View full text Add to dashboard Cite