PRODUCTION FLOW CONTROL FOR A MANUFACTURING SYSTEM WITH FLEXIBLE ROUTINGS

We study the dynamic routing problem for a flexible manufacturing system consisting of two unreliable machines and a finite buffer. One product-type is produced which requires two operations in sequence. The demand rate is assumed to be constant. Each machine is capable of performing both operations. The objective is to trace the demand while keeping the work-in-process low and the cycle-time short. An optimal control formulation is established for the dynamic routing problem. A production flow control algorithm is developed based on a combination of mathematical modeling and heuristics. The control policy is simulated and a comparison with the numerical optimal solution shows that it performs well for the instances under consideration.     

A simulation comparison on spatial Poisson mixed models

The statistical methods for analyzing spatial count data have often been based on random fields so that a latent variable can be used to specify the spatial dependence. In this article, we introduce two frequentist approaches for estimating the parameters of model-based spatial count variables. The comparison has been carried out by a simulation study. The performance is also evaluated using a real dataset and also by the simulation study. The simulation results show that the maximum likelihood estimator appears to be with the better sampling properties.     

Social Participation, Sense of Community and Social Well Being: A Study on American, Italian and Iranian University Students

Aim of the study was to assess the relationship between social participation and Sense of Community in a sample of University students and the impact of such variables on Social well being. A further aim was to assess the generality of the relationships between these constructs across different countries, and specifically, the USA, Italy and Iran. The sample includes 200 Italian, 125 American and 214 Iranian University students, male and female. Results show higher levels of social participation, Sense of Community and Social well being among American students. Sense of Community is positively correlated with social participation in all three samples; however, only among Italian students social participation positively predicts Social well being. Implications of results will be discussed.     

On a nonlinear Birnbaum–Saunders model based on a bivariate construction and its characteristics

Abstract

The Birnbaum-Saunders (BS) distribution is an asymmetric probability model that is receiving considerable attention. In this article, we propose a methodology based on a new class of BS models generated from the Student-t distribution. We obtain a recurrence relationship for a BS distribution based on a nonlinear skew–t distribution. Model parameters estimators are obtained by means of the maximum likelihood method, which are evaluated by Monte Carlo simulations. We illustrate the obtained results by analyzing two real data sets. These data analyses allow the adequacy of the proposed model to be shown and discussed by applying model selection tools.     

Geostatistical analysis of disease data: a case study of tuberculosis incidence in Iran

The main objective of this study is to introduce two advanced statistical methods and to consider geographical distribution of tuberculosis incidence in Iran. With the knowledge that environmental and climatic conditions in each region are affective for the incidence and spread of the disease, the study has been taken into consideration. The disease incidences in different counties are realizations of spatial data, therefore we apply the Poisson kriging and ordinary kriging for prediction of tuberculosis incidence rates map in Iran. To identify high risk areas using statistical map of disease, our results show that tuberculosis incidences are not uniformly distributed in whole of the country and estimated risk is high in the eastern parts. Assessing geographical distribution of a disease is essential for health officials to recognize high-risk areas, and improve case management and resource allocation.     

Import competition, employment and wages in U.S. manufacturing

This paper uses cointegration analysis to examine if employment and wages in the U.S. manufacturing sector exhibit any long run relationship with import competition. While overall the cointegration analysis supports the results reported in Revenga’s (1992) panel study it indicates that in the long run a positive correlation between import price and employment and/or a negative correlation between import price and wage are sector sensitive. There is also a considerable variation in the magnitude of employment and wage elasticities.     

Bayesian regression analysis with linked data using mixture normal distributions

The probabilistic uncertainty in record linkage affects statistical analysis such as regression analysis of linked data. This paper considers Bayesian regression analysis with linked data and shows that despite using the usual normal regression analysis, the least squares type estimators of regression coefficients are not always adequate. A method is proposed in which the distribution of the response variable is used. This method is related to finite mixture analysis and leads to more accurate estimations. A simple approach has been proposed to increase the tractability and reduce the number of mixture components. A Monte Carlo simulation study is also performed to assess the proposed approach.     

Cholesky-GARCH models with applications to finance

Instantaneous dependence among several asset returns is the main reason for the computational and statistical complexities in working with full multivariate GARCH models. Using the Cholesky decomposition of the covariance matrix of such returns, we introduce a broad class of multivariate models where univariate GARCH models are used for variances of individual assets and parsimonious models for the time-varying unit lower triangular matrices. This approach, while reducing the number of parameters and severity of the positive-definiteness constraint, has several advantages compared to the traditional orthogonal and related GARCH models. Its major drawback is the potential need for an a priori ordering or grouping of the stocks in a portfolio, which through a case study we show can be taken advantage of so far as reducing the forecast error of the volatilities and the dimension of the parameter space are concerned. Moreover, the Cholesky decomposition, unlike its competitors, decompose the normal likelihood function as a product of univariate normal likelihoods with independent parameters, resulting in fast estimation algorithms. Gaussian maximum likelihood methods of estimation of the parameters are developed. The methodology is implemented for a real financial dataset with seven assets, and its forecasting power is compared with other existing models.     

A modified two-sample t-test based on permutation method for large-scale data

In large-scale data, for example, analyzing microarray data, which includes hypothesis testing for equality of means in order to discover differentially expressed genes, often deals with a large number of features versus a few number of replicates. Furthermore, some genes are differentially expressed and some others not. Thus, a usual permutation method, which is applied facing these situations, estimates the p-value poorly. This is because two types of genes are mixed. To overcome this obstacle, the null permutation samples are suggested in the literatures. We propose a modified uniformly most powerful unbiased test for testing the null hypothesis.     

Estimation of the Parameters of a Two-phase Tandem Queue with a Second Optional Service

In this article, we consider a two-phase tandem queueing model with a second optional service. In this model, the service is done by two phases. The first phase of service is essential for all customers and after the completion of the first phase of service, any customer receives the second phase of service with probability α, or leaves the system with probability 1 ? α. Also, there are two heterogeneous servers which work independently, one of them providing the first phase of service and the other a second phase of service. In this model, our main purpose is to estimate the parameters of the model, traffic intensity, and mean system size, in the steady state, via maximum likelihood and Bayesian methods. Furthermore, we find asymptotic confidence intervals for mean system size. Finally, by a simulation study, we compute the confidence levels and mean length for asymptotic confidence intervals of mean system size with a nominal level 0.95.