Bayesian single and double variable sampling plans for the Weibull distribution with censoring

Chen, Jianwei; Li, Kim-Hung; Lam, Yeh

doi:10.1016/j.ejor.2005.11.023

Cited by 46 publications

(10 citation statements)

References 22 publications

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“…Some other strategies are also followed to design the sampling plans besides the statistical based paradigm, which include Bayesian approach and economic based approach. For further details, readers may refer Chen and Lam [17], Ferrell and Chhoker [18], Chen [19], Chen et al [20], Balamurali and Subramani [21], Vijayaraghavan and Sakthivel [22], Balamurali et al [23], and Fallahnezhad and Aslam [24]. In this paper, we have followed the designing methodology based on two points on the OC curve approach.…”

Section: Designing Methodology Of Variables Chain Sampling Planmentioning

confidence: 99%

Optimal Designing of Variables Chain Sampling Plan by Minimizing the Average Sample Number

Balamurali

Usha

2013

International Journal of Manufacturing Engineering

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We investigate the optimal designing of chain sampling plan for the application of normally distributed quality characteristics. The chain sampling plan is one of the conditional sampling procedures and this plan under variables inspection will be useful when testing is costly and destructive. The advantages of this proposed variables plan over variables single sampling plan and variables double sampling plan are discussed. Tables are also constructed for the selection of optimal parameters of known and unknown standard deviation variables chain sampling plan for specified two points on the operating characteristic curve, namely, the acceptable quality level and the limiting quality level, along with the producer’s and consumer’s risks. The optimization problem is formulated as a nonlinear programming where the objective function to be minimized is the average sample number and the constraints are related to lot acceptance probabilities at acceptable quality level and limiting quality level under the operating characteristic curve.

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Section: Designing Methodology Of Variables Chain Sampling Planmentioning

confidence: 99%

Optimal Designing of Variables Chain Sampling Plan by Minimizing the Average Sample Number

Balamurali

Usha

2013

International Journal of Manufacturing Engineering

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“…Related investigations can be found in [9,27]. However, studies that have used Bayesian methods often assumed that either one of the parameters of the Weibull power law intensity of an NHPP is known or both parameters are independent [8,9,13,15,19].…”

Section: Introductionmentioning

confidence: 98%

Nonperiodic preventive maintenance for repairable systems

Lin

Huang

2010

Naval Research Logistics

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As a complex system with multiple components usually deteriorates with age, preventive maintenance (PM) is often performed to keep the system functioning in a good state to prolong its effective age. In this study, a nonhomogeneous Poisson process with a power law failure intensity is used to describe the deterioration of a repairable system, and the optimal nonperiodic PM schedule can be determined to minimize the expected total cost per unit time. However, since the determination of such optimal PM policies may involve numerous uncertainties, which typically make the analyses difficult to perform because of the scarcity of data, a Bayesian decision model, which utilizes all available information effectively, is also proposed for determining the optimal PM strategies. A numerical example with a real failure data set is used to illustrate the effectiveness of the proposed approach. The results show that the optimal schedules derived by Bayesian approach are relatively more conservative than that for non-Bayesian approach because of the uncertainty of the intensity function, and if the intensity function are updated using the collected data set, which indicates more severe deterioration than the prior belief, replacing the entire system instead of frequent PM activities before serious deterioration is suggested.

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“…Email: cweiwu@ie.nthu.edu.tw the literature, a number of acceptance sampling plans have been developed for a wide variety of situations. Some recent references are Balamurali and Jun, [2] Pearn and Wu, [3] Chen et al, [4] Wu and Pearn, [5] Aslam et al, [6] Seo et al, [7] Liu and Wu, [8] Liu et al, [9] Negrin et al, [10,11] Fernández et al, [12] Wu [13] and Wu et al [14] In testing of certain products, the test procedures are not always accurate or obtaining a random sample is difficult. In such cases, resampling is always done.…”

Section: Introductionmentioning

confidence: 99%

A flexible sampling scheme for variables inspection with loss consideration

Chen

2015

Journal of Statistical Computation and Simulation

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This article develops a variables sampling scheme for resubmitted lots by incorporating the concept of Taguchi loss function. The probability of lot acceptance is derived based on the exact sampling distribution and two-point condition on operating characteristic curve is used to determine the plan parameters that meet both the producer's and consumer's quality and risk requirements. Moreover, the performance of the proposed variables resubmitted sampling plan is investigated and compared with the classical variables single sampling plan. The results indicate that the developed resubmitted sampling plan can provide the same protection with less inspection when the submitted lot is good enough. Tables of the plan parameters under various conditions are provided and the use of the proposed plan is also illustrated with an example.

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Bayesian single and double variable sampling plans for the Weibull distribution with censoring

Cited by 46 publications

References 22 publications

Optimal Designing of Variables Chain Sampling Plan by Minimizing the Average Sample Number

Optimal Designing of Variables Chain Sampling Plan by Minimizing the Average Sample Number

Nonperiodic preventive maintenance for repairable systems

A flexible sampling scheme for variables inspection with loss consideration

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