SYNOPTIC ABSTRACTIn this article, we consider a parallel machine scheduling problem that integrates partial maintenance activities due to job-dependent machine deteriorations. In this problem, we are given identical parallel machines, each with an initial maintenance level which is a non-negative integer value to represent its maintenance state, and a set of jobs each with a non-preemptive processing time and a machine deterioration. For each job, its machine deterioration qualifies the decrement of the machine maintenance level after processing it. When each machine processes jobs, one should guarantee a non-negative maintenance level at any time point. Thus, whenever necessary, a maintenance activity must be performed to restore the machine maintenance level. The goal of this problem is to schedule the jobs and the maintenance activities, such that the makespan achieves the minimum. We take the partial maintenance activity pattern, in which each activity can be performed to increase the machine maintenance level to any level not exceeding the maximum. To tackle this problem, we first model it by an integer programming formulation, and then propose an LP-based approximation algorithm with an instance-dependent approximation factor and show the approximability lower bound. Finally, we present a fast heuristic algorithm followed by a numerical example to show how this algorithm works.

SYNOPTIC ABSTRACTThis article deals with stochastic comparisons of the series and parallel systems comprising Kumaraswamy generalized family of distributions. The results are established in two directions. We consider systems with different model parameters, and obtain some ordering results under the condition of multivariate chain majorization. Next, we use the notion of vector majorization and other related orders to establish various stochastic orders, such as the usual stochastic order, the likelihood ratio order, and the dispersive order. Further, results are obtained when components of two systems have independent and heterogeneous Kumaraswamy generalized family of distributions with different parent distribution functions. Numerical examples and counterexamples are provided to illustrate the established results.

SYNOPTIC ABSTRACTIn many practical applications, there is a need for testing of an hypothesis under order restrictions. We propose a general class of tests which generalizes the tests proposed by Jonckheere (1954), Kumar et al. (1994a, b), and Patil (2007). The asymptotic distribution of test statistic under null hypothesis, as well as under an equally spaced sequence of local alternatives, is derived. Pitman asymptotic relative efficiency comparisons with respect to competing tests are made to evaluate the performance of the proposed class of tests. An example is provided in Hundal (1969) data for implementing the proposed test procedure. Furthermore, a simulation study is carried out to estimate the power and level of significance of the proposed class of tests.

SYNOPTIC ABSTRACTKozubowski and Podgorski (2003) have discussed properties, characterizations, and estimation of parameters of skew log Laplace distribution (SLLD). In this article, classical optimum tests for scale parameter of SLLD are derived. The most powerful (MP) test is obtained for scale parameter when shape parameters are known. Wald’s sequential probability ratio test (SPRT) is obtained, and its properties are studied. The likelihood ratio tests (LRT) for scale parameter are derived when the shape parameters are known and unknown. Finally, the SPRT and LRT are illustrated to the real life data.

SYNOPTIC ABSTRACTIn survival analysis and reliability, researchers are often interested in assessing the effects of different stress factors on the lifetime of experimental units. The model introduced in this article is motivated by a study of the effects of altitude and other risk factors on decompression sickness, a condition encountered when individuals are exposed to significant changes in environmental pressure. Unlike standard life-testing experiments, in this study, the levels of the stress factor, viz. altitude, are changed during the exposure duration. This is known as a step-stress test, a class of accelerated testing, widely used in material testing. Recently Kannan, Kundu, and Balakrishnan (2010) introduced the cumulative risk model as an alternative to the widely used cumulative exposure model. The new model allows for the inclusion of a lag period in the hazard function, a more realistic assumption in most applications. In this article, we consider the cumulative risk model assuming that the lifetime distributions of the experimental units follow Weibull distributions at the different levels of the risk factor. It is assumed that the level of the stress factor is changed only once during the exposure duration at a pre-fixed time τ1. The maximum likelihood and the least squares methods have been used to estimate the unknown parameters. Monte Carlo simulations are performed to compare the performances of the two different methods. We further propose the Bayes estimators of the unknown parameters of the model. To evaluate the performance of the model, one data set from the altitude decompression sickness experiment has been analyzed, and it is observed that the proposed model fits the data quite well.

SYNOPTIC ABSTRACTIn this study, a fuzzy multiple deferred state (FMDS) attribute sampling plan is proposed when the proportion parameter (p) is uncertain. In this plan, the decision upon the acceptance of the submitted lot is based on both current lot information and future successive lots information. Then, the authors prefer to discuss the proposed plan more in a specific case designated as FMDS(0,1). Tables have been prepared to construct and determine parameters of the FMDS(0,1) plan by use of the two-point approach for determining the plan selection. In order to be fully informed, some applicable examples are given. Also, the proposed plan is compared with the existing fuzzy single, fuzzy double, and fuzzy sequential sampling plans. The obtained results show that the main advantage of the FMDS(0,1) plan is to have a minimum average sample number under the same conditions. Moreover, the authors show that the proposed plan is well-defined since it turns into a traditional case when p is crisp.

SYNOPTIC ABSRACTThis article is concerned with the problem of predicting future observables from the Weibull-geometric model based on progressively Type-II censored data. The Bayes point predictors and the Bayesian prediction intervals are obtained. The one and two-sample prediction techniques are considered. Numerical computations are given to illustrate the performance of the procedures.

SYNOPTIC ABSTRACTThe purpose of this study is to examine the effect of workplace ostracism on employee voice behavior. We also distinguish the influence of supervisor ostracism from that of coworker ostracism and explore the moderating effects of employee traditionality and power distance. A supervisor–subordinate matching survey was conducted at 22 enterprises in China. Approximately 262 valid samples were collected. The results of hierarchical regression analyses demonstrate that supervisor ostracism has a negative impact on employee voice behavior. However, coworker ostracism has no effect on employee voice behavior. In addition, the negative relationship between supervisor ostracism and employee voice behavior was weaker for employees with high traditionality. Finally, employee power distance mitigates the extent of the negative relationship between supervisor ostracism and employee voice behavior.

SYNOPTIC ABSTRACTWithin both the common- and private-value paradigms, statistical inferences for the expected winning bids are considered. Under the common-value setting, Weibull and normal distributions are considered for modeling the cost estimates whereas Weibull, Pareto, and exponential distributions are considered under the private-value setting. Using the recently introduced Generalized Variable Method (GVM), we propose generalized tests and generalized confidence intervals for the expected winning bids for risk-neutral sealed-bid auctions with common- and private-value bidders under various bidders’ cost-estimate distributions. A simulation study is described to illustrate the proposed procedures.

SYNOPTIC ABSTRACTWe expand the Merton's structural credit risk model into a model that includes an underlying asset process with a non-Gaussian and serially correlated stochastic nature. Using a standard Edgeworth expansion, we arrive at closed-form analytic expressions for the probability of default, the distance to default, and the term structure of credit spread, allowing us to evaluate more accurately the credit risk of incorporating nonnormal asset returns. Moody's KMV (Kealhofer, McQuown, and Vasicek) analogous procedures are proposed for the estimation of the model parameters. Empirical applications for credit-risk evaluation are illustrated, which reveal the significant effects on credit risk due to the non-Gaussianity of the underlying firm's asset process.