Volume 34, Issue 2

The production network under examination consists of a number of workstations. Each workstation is a parallel configuration of machines performing the same kind of tasks on a given part. Parts move from one workstation to another and at each workstation a part is assigned randomly to a machine. We assume that the production network is acyclic , that is, a part does not return to a workstation where it previously received service. Furthermore, we assume that the quality of the end product is additive , that is, the sum of the quality contributions of the machines along the production path. The contribution of each machine is modeled by a separate random variable. Our main result is the construction of estimators that allow pairwise and multiple comparison of the means and variances of machines in the same workstation. These comparisons then may lead to the identification of unreliable machines. We also discuss the asymptotic distributions of the estimators that allow the use of standard statistical tests and decision making.

Recently, G. Rajesh, E. I. Abdul-Sathar and S. Nair Rohini [G. Rajesh, E. I. Abdul-Sathar and S. Nair Rohini, On dynamic weighted survival entropy of order α, Comm. Statist. Theory Methods 46 2017, 5, 2139–2150] proposed a measure of uncertainty based on the survival function called weighted survival entropy of order α. They have also introduced the dynamic form of a measure called dynamic weighted survival entropy of order α and studied various properties in the context of reliability modeling. In this paper, we extend these measures into the bivariate setup and study its properties. We also look into the problem of extending the same measure for conditionally specified models. Empirical and non-parametric estimators are suggested for the proposed measure using the conditionally specified model, and the effect of the proposed estimators is illustrated using simulated and real data sets.

The concept of sequential order statistics were introduced by Kamps in 1995. In this article, we derive an acceptance sampling plan, for units having exponentially distributed lifetime, using sequential order statistics. Based on data obtained from progressive type II censoring using constant stress accelerated life tests, we obtain the maximum likelihood estimates of the parameters of the exponential distribution. Further, a log linear life-stress relationship is assumed to derive the exact distributions of the estimators of the parameters of exponential distribution. The parameters of the sampling scheme are obtained by minimizing expected total testing cost satisfying usual probability requirements. Some numerical results are presented in a table to illustrate our plans.

This paper deals with a new method of deriving the Process Capability Index (PCI) when the quality characteristic X follows a positively skewed distribution. The focus of the paper is to derive a new estimate of PCI by taking into account the 100⁢(1-α){100(1-{\alpha})}  Confidence Intervals (CI) of the parameter ( s ) and arriving at a new expression. The formula Cs{{C}_{{s}}}, proposed by Wright (1995) which contains a component for skewness, is reexamined and a new estimate is constructed by utilizing the lower, middle and upper values of the CI of the parameter. The weighted average of the three possible estimates of Cs{{C}_{{s}}} is proposed as the new estimate by taking the weights inversely proportional to the squared deviation from the hypothetical value of Cs{{C}_{{s}}}. The properties of the estimate are studied by simulation using one parameter exponential distribution.

Simultaneous optimization of multiple quality characteristics (responses) of a product or process is required in many real-world problems. Multiresponse optimization (MRO) techniques tries to solve such problems; the ultimate objective of which is to adjust control factors that provides most desired values for the responses. Regression techniques are most commonly used methods to identify and estimate relationships between control variables and responses. Due to the industrial advances and hence the complexity of processes and systems, many relationships between input variables and quality characteristics have become much more complex. In such circumstances, classic regression techniques encounter difficulties to create a well-fitted model which can be easily optimized. The alternative approach proposed in this study is a regression tree method called CART, which is a data mining method. Since the output of CART consists of several if-then terms, NSGA-II algorithm was considered to solve the model and achieve the optimal solutions. Finally, we evaluate performance of the proposed method with a real data set about modeling and improvement of automotive engines.

In the area of stress-strength models, there has been a large amount of work regarding the estimation of the reliability R=Pr⁡(X<Y){R=\Pr(X<Y)}. The algebraic form for R=Pr⁡(X<Y){R=\Pr(X<Y)} has been worked out for the vast majority of the well-known distributions when X and Y are independent random variables belonging to the same univariate family. In this paper, forms of R are considered when (X,Y){(X,Y)} follow bivariate distributions with dependence between X and Y . In particular, explicit expressions for R are derived when the joint distribution are dependent bivariate beta and bivariate Kumaraswamy. The calculations involve the use of special functions.