Evaluation of scheduling strategies for a dynamic job shop in a tool-shared,flexible manufacturing environment

A two-phase approach is used to examine the impact of job scheduling rules and tool selection policies for a dynamic job shop system in a tool-shared, flexible manufacturing environment. The first phase develops a generalized simulation model and analyses 'simple' job scheduling rules and tool selection policies under various operating scenarios. The results from this investigation are then used to develop and analyse various bi-criteria rules in the second phase of this study. The results show that the scheduling rules have the most significant impact on system performance, particularly at high shop load levels. Tool selection policies affect some of the performance measures, most notably, proportion of tardy jobs, to a lesser degree. Higher machine utilizations can be obtained at higher tool duplication levels but at the expense of increased tooling costs and lower tool utilization. The results also show that using different processing time distributions may have a significant impact on shop performance.     

A nonparametric hypothesis test for heteroscedasticity

In this paper, a hypothesis test for heteroscedasticity is proposed in a nonparametric regression model. The test statistic, which uses the residuals from a nonparametric fit of the mean function, is based on an adaptation of the well-known Levene's test. Using the recent theory for analysis of variance when the number of factor levels goes to infinity, the asymptotic distribution of the test statistic is established under the null hypothesis of homocedasticity and under local alternatives. Simulations suggest that the proposed test performs well in several situations, especially when the variance is a nonlinear function of the predictor.     

Subsampling-extrapolation bandwidth selection in bivariate kernel density estimation

This paper focuses on bivariate kernel density estimation that bridges the gap between univariate and multivariate applications. We propose a subsampling-extrapolation bandwidth matrix selector that improves the reliability of the conventional cross-validation method. The proposed procedure combines a U-statistic expression of the mean integrated squared error and asymptotic theory, and can be used in both cases of diagonal bandwidth matrix and unconstrained bandwidth matrix. In the subsampling stage, one takes advantage of the reduced variability of estimating the bandwidth matrix at a smaller subsample size m (m < n); in the extrapolation stage, a simple linear extrapolation is used to remove the incurred bias. Simulation studies reveal that the proposed method reduces the variability of the cross-validation method by about 50% and achieves an expected integrated squared error that is up to 30% smaller than that of the benchmark cross-validation. It shows comparable or improved performance compared to other competitors across six distributions in terms of the expected integrated squared error. We prove that the components of the selected bivariate bandwidth matrix have an asymptotic multivariate normal distribution, and also present the relative rate of convergence of the proposed bandwidth selector.     

Prediction of future failures times based on Type-I hybrid censored samples of random sample sizes

In this article, we consider the prediction of future failure times based on Type-I hybrid censored samples. Point predictors and prediction intervals using different procedures are discussed for a general model. The exponential and Rayleigh distributions are used as illustrative examples to show the most simplified forms of the so obtained predictors as well as prediction intervals. Intensive simulation study and a real life dataset are presented to illustrate our findings and results.     

How Many More Missing Women? Excess Female Mortality and Prenatal Sex Selection, 1970–2050

Sex‐based discrimination has resulted in severe demographic imbalances between males and females, culminating in a large number of “missing women” in several countries around the world. We provide new estimates and projections of the number of missing females and of the roles played by prenatal and postnatal factors in this imbalance. We estimate time series of the number of missing females, the number of excess female deaths, and the number of missing female births for the world and selected countries. Estimates are provided for 1970–2010 and projections are made from 2010 to 2050. We show that the estimates of these different indicators are consistent with one another and account for the dynamics of the population of missing females over time. We conclude that the number of missing females has steadily risen in the past decades, reaching 126 million in 2010, and the number is expected to peak at 150 million in 2035. Excess mortality was the dominant cause of missing females in the past, and this is expected to remain the case in future decades in spite of the recent rise of prenatal sex selection. The annual number of newly missing females reached 3.4 million in 2010 and is expected to remain above 3 million every year until 2050.     

Moving targets: determinants of nutritional preferences and habitat use in an urban ant community

Urban environments are often associated with reduced biodiversity, presumably because they are typically more fragmented, warmer, and drier than nearby non-urban environments. However, urban landscapes offer significant complexity that have allowed some taxonomic groups to flourish. Understanding how urban-exploiting animals navigate this spatiotemporal heterogeneity is important given the continued global urban land expansion. Here, we examined the factors influencing resource-use in an urban community of ants, which represent a widespread and important taxon in urban ecosystems. In particular, we sought to integrate ants’ nutritional, thermal, and spatial niches to better understand how urban animals successfully access critical resources throughout their active season. Meteorological season (spring, summer, and fall) and/or species (n?=?9) influenced ants’ preferences for nutrition (ratio of ingested protein-to-carbohydrate ratio), as well as the temperature, type (impervious vs. non-impervious), and shade status (shaded vs. non-shaded) of surfaces used during activity. Our data also indicate links among habitat variables, as well as between nutritional preferences and habitat use. Together, our results suggest that species and seasonality influence ecological (combined nutritional, thermal, and spatial) niches in an urban community. We encourage future work in urban ecosystems that continues to integrate more features of the ecological niche, and to examine the outcomes of variation in niches (e.g., non-overlapping niches may explain both the persistence of some native animals and the success of invaders).     

Local influence in seemingly unrelated regression model with ridge estimate

Local influence is a well-known method for identifying the influential observations in a dataset and commonly needed in a statistical analysis. In this paper, we study the local influence on the parameters of interest in the seemingly unrelated regression model with ridge estimation, when there exists collinearity among the explanatory variables. We examine two types of perturbation schemes to identify influential observations: the perturbation of variance and the perturbation of individual explanatory variables. Finally, the efficacy of our proposed method is illustrated by analyzing [13 A. Munnell, Why has productivity declined? Productivity and public investment, New Engl. Econ. Rev. (1990), pp. 3–22. [Google Scholar]] productivity dataset.     

A new family of multivariate slash distributions

In this article, a new form of multivariate slash distribution is introduced and some statistical properties are derived. In order to illustrate the advantage of this distribution over the existing generalized multivariate slash distribution in the literature, it is applied to a real data set.