Threshold switch over policy for multi-component machining system with controllable rates

Jain, Madhu; Rani, Varsha; Kumari, Rekha

doi:10.1504/ijise.2015.070180

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…The reliability analysis for a retrial queueing system with mixed standby has been presented by Kuo et al [90]. The performance analysis of a multi component MRP under threshold policy and the provision of mixed (cold and warm) standbys has been suggested by Jain et al [74]. They considered failure and repair rates to be interdependent and controllable.…”

Section: Queueing Models With Mixed Standbysmentioning

confidence: 99%

Survey on queueing models with standbys support

Kolledath

Kumar

Pippal³

2018

YUGOSLAV J OPERATION

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This paper is a survey article on queueing models with standbys support. Due to many real life applications of queueing models, it has become an interesting area for researchers and a lot of research work has been exerted so far. It is worthwhile to examine the performance based analysis for queueing modelling system as it provides a valuable insight to the tractability of the system and accelerates its efficiency. The provision of standbys to the queueing modelling of a real system is needed for smooth functioning in the presence of its unavoidable failures. The present survey provides a dig into the research work done, and emphasis the sequential developments on queueing models with standbys support.

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Section: Queueing Models With Mixed Standbysmentioning

confidence: 99%

Survey on queueing models with standbys support

Kolledath

Kumar

Pippal³

2018

YUGOSLAV J OPERATION

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“…In this investigation, they incorporated the controlled rates for the Markov modeling of a machine repair system, operating under N -policy and supported by additional repairman apart from regular repairman. Further, Jain et al [18] investigated a multi component machine repair problem with controllable failure and repair rates and provided the comparison of numerical results obtained by product type technique with those results obtained by neuro-fuzzy technique.…”

Section: Introductionmentioning

confidence: 99%

N-policy for a repairable redundant machining system with controlled rates

Jain

Shekhar

Shukla³

2016

RAIRO-Oper. Res.

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The present investigation deals with a multi-component machining system operating under N -policy. There is a provision of k type of mixed standbys units and maintenance crew consisting of permanent repairman as well as removable additional repairmen. The life time and repair time of the units are exponentially distributed with interdependent rates. The permanent repairman can start the repair only when N failed units are accumulated in the system. As soon as a unit fails, it is replaced by an available standby unit. In case when all the standby units are exhausted, the machining system starts to function in a degraded mode due to overload. Markov model is developed by constructing the governing transient state equations which are solved by using Runge−Kutta method. Various performance measures viz. queue size distribution, expected number of failed units, cost function, etc. are evaluated. By taking numerical illustration, numerical results are computed and also depicted with the help of graphs and figures.

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Threshold switch over policy for multi-component machining system with controllable rates

Cited by 2 publications

References 27 publications

Survey on queueing models with standbys support

Survey on queueing models with standbys support

N-policy for a repairable redundant machining system with controlled rates

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