Simultaneous optimization of [Xbar] control chart and age-based preventive maintenance policies under an economic objective

Yeung, Thomas; Cassady, C. Richard; Schneider, Kellie

doi:10.1080/07408170701592515

Cited by 51 publications

(25 citation statements)

References 25 publications

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“…Cassady et al (2000) studied X-chart in conjunction with an age-based preventive maintenance policy. Yeung et al (2008) modified the model suggested by Cassady et al (2000) for using an X-chart to monitor the output of the production process and to determine when to perform corrective, condition-based maintenance so as to optimise the sample size, control chart parameters, and time to interval for performing PM. Rahim and Ben-Daya (2001) provided an overview of the literature dealing with integrated models for production, quality, and maintenance.…”

Section: Literature Reviewmentioning

confidence: 99%

A methodology for simultaneous optimisation of design parameters for the preventive maintenance and quality policy incorporating Taguchi loss function

Pandey

Kulkarni

Vrat

2012

International Journal of Production Research

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The performance of a production system depends on the breakdown-free operation of equipment and processes. Maintenance and quality control play an important role in achieving this goal. In addition to deteriorating with time, equipment may experience a quality shift (i.e. process moves to out-of-control state), which is characterised by a higher rejection rate and a higher tendency to fail. This paper develops an integrated model for joint optimisation of preventive maintenance interval and control parameters incorporating the Taguchi loss function. We consider two types of maintenance policies: minimal corrective maintenance that maintains the state of the equipment without affecting the age and imperfect preventive maintenance that upgrades the equipment in between 'as good as new' and 'as bad as old' condition. The proposed model enables the determination of the optimal value of each of the four decision variables, i.e. sample size (n), sample frequency (h), control limit coefficient (k), and preventive maintenance interval (t PM ) that minimises the expected total cost of the integration per unit time. A numerical example is presented to demonstrate the effect of the cost parameters on the joint economic design of preventive maintenance and process quality control policy. The sensitivity of the various parameters is also examined.

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Section: Literature Reviewmentioning

confidence: 99%

A methodology for simultaneous optimisation of design parameters for the preventive maintenance and quality policy incorporating Taguchi loss function

Pandey

Kulkarni

Vrat

2012

International Journal of Production Research

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“…They present a model using age-replacement where a machine is taken out for preventive maintenance and replaced by a standby one if its age reaches a certain value. Yeung et al 8 extend the initial preliminary investigation of integrating the economic design of control charts and the optimization of preventive maintenance policies of using anX chart in conjunction with an age-replacement preventive maintenance policy. They formulate a partially observable, discrete-time Markov decision process in order to obtain the near-optimal combined preventive maintenance/statistical process control policy that minimizes the costs associated with maintenance, sampling, and poor quality.…”

Section: Introductionmentioning

confidence: 98%

A study of time‐between‐events control chart for the monitoring of regularly maintained systems

Khoo

Xie

2008

Quality & Reliability Eng

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Owing to usage, environment and aging, the condition of a system deteriorates over time. Regular maintenance is often conducted to restore its condition and to prevent failures from occurring. In this kind of a situation, the process is considered to be stable, thus statistical process control charts can be used to monitor the process. The monitoring can help in making a decision on whether further maintenance is worthwhile or whether the system has deteriorated to a state where regular maintenance is no longer effective. When modeling a deteriorating system, lifetime distributions with increasing failure rate are more appropriate. However, for a regularly maintained system, the failure time distribution can be approximated by the exponential distribution with an average failure rate that depends on the maintenance interval. In this paper, we adopt a modification for a time-between-events control chart, i.e. the exponential chart for monitoring the failure process of a maintained Weibull distributed system. We study the effect of changes on the scale parameter of the Weibull distribution while the shape parameter remains at the same level on the sensitivity of the exponential chart. This paper illustrates an approach of integrating maintenance decision with statistical process monitoring methods.

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“…Pandey, Kulkarni, and Vrat (2010) contend that a relationship exists between maintenance management and SPC. Some studies on the integration of maintenance management and SPC can be found in Yeung, Cassady, and Schneider (2007), Radhoui, Rezg, and Chelbi (2009), Panagiotidou and Tagaras (2008, Wang (2012), and so on.…”

Section: Introductionmentioning

confidence: 99%

Integration of SPC and performance maintenance for supply chain system

Zhong

2016

International Journal of Production Research

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In this paper, a supply chain system is viewed as a maintainable system, and the economic-statistical design of a likelihood ratio control chart with a maintenance application is considered for this system. The supply chain system is described by a three-state: normal state, warning state and failure state. A likelihood ratio control chart is used to monitor the system given that only categorical observations can be obtained. When the chart signals, a full inspection is performed to determine the actual system state (normal or warning), and preventive maintenance is immediately performed in the warning state. In addition, the supply chain system must be corrected upon failure (i.e. corrective maintenance), and should be maintained in a scheduled time (i.e. planned maintenance). A mathematical model is developed for the joint optimisation of the control chart parameters and planned maintenance time based on renewal theory. An example is presented to illustrate how to determine the optimal design parameters. We also investigate the effect of coefficients and statistical constraints on the decision variables and the expected cost.

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Simultaneous optimization of [Xbar] control chart and age-based preventive maintenance policies under an economic objective

Cited by 51 publications

References 25 publications

A methodology for simultaneous optimisation of design parameters for the preventive maintenance and quality policy incorporating Taguchi loss function

A methodology for simultaneous optimisation of design parameters for the preventive maintenance and quality policy incorporating Taguchi loss function

A study of time‐between‐events control chart for the monitoring of regularly maintained systems

Integration of SPC and performance maintenance for supply chain system

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