A survey of the machine interference problem

Haque, Lani; Armstrong, Michael J.

doi:10.1016/j.ejor.2006.02.036

Cited by 136 publications

(53 citation statements)

References 140 publications

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“…Haque & Armstrong 2007;Jain et al, 2010;Krishnamoorthy et al, 2014;Chandrasekaran et al 2016). Many researchers have worked on the problem of machining system with standby machines (cf.…”

Section: Literature Reviewmentioning

confidence: 99%

Sensitivity analysis of repairable redundant system with switching failure and geometric reneging

Shekhar¹,

Jain²,

Raina³

et al. 2017

10.5267/j.dsl

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This study deals with the performance modeling and reliability analysis of a redundant machining system composed of several functional machines. To analyze the more realistic scenarios, the concepts of switching failure and geometric reneging are included. The time-tobreakdown and repair time of operating and standby machines are assumed to follow the exponential distribution. For the quantitative assessment of the machine interference problem, various performance measures such as mean-time-to-failure, reliability, reneging rate, etc. have been formulated. To show the practicability of the developed model, a numerical illustration has been presented. For the practical justification and validity of the results established, the sensitivity analysis of reliability indices has been presented by varying different system descriptors.Growing Science Ltd. All rights reserved. 7

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“…Haque & Armstrong 2007;Jain et al, 2010;Krishnamoorthy et al, 2014;Chandrasekaran et al 2016). Many researchers have worked on the problem of machining system with standby machines (cf.…”

Section: Literature Reviewmentioning

confidence: 99%

Sensitivity analysis of repairable redundant system with switching failure and geometric reneging

Shekhar¹,

Jain²,

Raina³

et al. 2017

10.5267/j.dsl

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“…With respect to service outages and server unreliability, we face a vast amount of queueing literature. Surveys on the machine interference problem and server unreliability may be found in Stecke and Aronson (1985) and Haque and Armstrong (2007). Unreliable servers are often modeled using vacation models.…”

Section: Modeling Arrival Rate and Natural Service Timesmentioning

confidence: 99%

Modeling a Hospital Queueing Network

Creemers¹,

Lambrecht²

2010

International Series in Operations Research &Amp; Management Science

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-Healthcare systems differ intrinsically from manufacturing systems. As such, they require a distinct modeling approach. In this article, we show how to construct a queueing network of a general class of healthcare systems. In order to analyze such networks, we use the parametric decomposition approach. Using this approach the network is decomposed into a set of single queueing systems which can be analyzed separately. Afterwards, results of these single queueing systems can be aggregated and general performance measures of the queueing network are obtained. In addition, we develop new expressions to assess the impact of service outages and use the queueing network to approximate patient flow times and to evaluate a number of practical applications.

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“…In queueing and reliability literature, the notable research works on Markov modeling of machining system with standby support can be found [cf. Wang and Kuo (2000), Wang and Ke (2003), and Haque and Armstrong (2007)]. Shree et al (2015) proposed a Markov model for the machining systems with hot spares.…”

Section: Introductionmentioning

confidence: 99%

Vacation model for Markov machine repair problem with two heterogeneous unreliable servers and threshold recovery

Jain

Meena

2017

J Ind Eng Int

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Markov model of multi-component machining system comprising two unreliable heterogeneous servers and mixed type of standby support has been studied. The repair job of broken down machines is done on the basis of bi-level threshold policy for the activation of the servers. The server returns back to render repair job when the prespecified workload of failed machines is build up. The first (second) repairman turns on only when the work load of N 1 (N 2 ) failed machines is accumulated in the system. The both servers may go for vacation in case when all the machines are in good condition and there are no pending repair jobs for the repairmen. Runge-Kutta method is implemented to solve the set of governing equations used to formulate the Markov model. Various system metrics including the mean queue length, machine availability, throughput, etc., are derived to determine the performance of the machining system. To provide the computational tractability of the present investigation, a numerical illustration is provided. A cost function is also constructed to determine the optimal repair rate of the server by minimizing the expected cost incurred on the system. The hybrid soft computing method is considered to develop the adaptive neuro-fuzzy inference system (ANFIS). The validation of the numerical results obtained by Runge-Kutta approach is also facilitated by computational results generated by ANFIS.

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A survey of the machine interference problem

Cited by 136 publications

References 140 publications

Sensitivity analysis of repairable redundant system with switching failure and geometric reneging

Sensitivity analysis of repairable redundant system with switching failure and geometric reneging

Modeling a Hospital Queueing Network

Vacation model for Markov machine repair problem with two heterogeneous unreliable servers and threshold recovery

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