A heuristic algorithm for the m-machine,n-job flow-shop sequencing problem

In a general flow-shop situation, where all the jobs must pass through all the machines in the same order, certain heuristic algorithms propose that the jobs with higher total process time should be given higher priority than the jobs with less total process time. Based on this premise, a simple algorithm is presented in this paper, which produces very good sequences in comparison with existing heuristics. The results of the proposed algorithm have been compared with the results from 15 other algorithms in an independent study by Park [13], who shows that the proposed algorithm performs especially well on large flow-shop problems in both the static and dynamic sequencing environments.     

Money Matters: Young Adults’ Perception of the Economic Consequences of their Parents’ Divorce

A qualitative study examined the perceptions of 22 Israeli young adults (ages 20–25) of childhood parental divorce. Respondents discussed their experiences, including economic consequences of the divorce. Results related to the practical aspect of economic decline, to economic issues as embodiment of parental conflicts, and to children’s emotional and practical roles connected to economic changes. Children’s understanding and coping with financial issues are related to three profiles of overall adjustment identified in this study—resilience, survival, and vulnerability. Resilient young adults interpreted as empowering their understanding and coping; the survivors recognized their efforts as meaningful but burdensome; and vulnerable participants felt that economic changes caused a heavy financial and emotional price. Limitations and implications are discussed.

Everyday people: public identities in contemporary hip-hop culture

As scholars on culture have suggested elsewhere, cooption through consumption by the music culture industry has forced those who want to be part of the hip-hop subculture to go back underground. But to label all of these individuals in the underground merely ‘hip-hop’ would be as equally uninformative as labeling anyone who wears baggy blue jeans and can rhyme words together as being hip-hop. My year of field research in Chicago revealed that five major identities emerged: Professional Headz, Refugee Headz, Hip-Hop Fundamentalists, Black Headz and Tech Headz. These identities were characterized by the personal values that my participants indicated were most reaffirmed through hip-hop culture. This article contributes to the literature on hip-hop by providing an ethnographic study on how the Hip-Hop Nation defines itself, while examining some of the tensions that these identities provide within the Hip-Hop Nation. It also reinforces the view that subculture must still be experienced in the flesh and is not fully experienced by being able to engage in the culture industry of hip-hop through purchasing records, watching videos or buying hip-hop fashions. This article also provides a more complex qualitative understanding of what values constitute being hip-hop and which ones do not.     

Migration,Diaspora and Citizenship: A Qualitative Study of the Perceptions of Pakistani Nationals towards the Political Rights of Pakistani Dual Citizens

The dual citizenship has been the subject of intense political debate in Pakistan barring the elected representatives, in parliament, the provincial assemblies and the presidency, from holding dual nationality. The perceptions that holding a foreign citizenship challenges the undiluted loyalty to country further engender question mark over their political participation in country’s affairs. A qualitative study has been carried out in the city of Rawalpindi to explore the stance of Pakistani nationals on such exclusion of Pakistani dual citizens from mainstream politics of Pakistan asking should the individual’s association to the state be an exclusive one? In this paper, in‐depth interviews were conducted with a sample of 40 male and female respondents (22 Pakistani citizens, 18 Pakistani dual citizens) and 6 constitutional experts. The results indicate distrust among Pakistani citizens when considering dual citizens’ right of representation in general elections of Pakistan, stemming from a mistrust of their “split loyalties”.     

Inference under Heteroscedasticity of Unknown Form Using an Adaptive Estimator

The heteroscedasticity consistent covariance matrix estimators are commonly used for the testing of regression coefficients when error terms of regression model are heteroscedastic. These estimators are based on the residuals obtained from the method of ordinary least squares and this method yields inefficient estimators in the presence of heteroscedasticity. It is usual practice to use estimated weighted least squares method or some adaptive methods to find efficient estimates of the regression parameters when the form of heteroscedasticity is unknown. But HCCM estimators are seldom derived from such efficient estimators for testing purposes in the available literature. The current article addresses the same concern and presents the weighted versions of HCCM estimators. Our numerical work uncovers the performance of these estimators and their finite sample properties in terms of interval estimation and null rejection rate.     

Estimation of finite population variance in successive sampling using multi-auxiliary variables

ABSTRACT

In this paper, a general class of estimators for estimating the finite population variance in successive sampling on two occasions using multi-auxiliary variables has been proposed. The expression of variance has also been derived. Further, it has been shown that the proposed general class of estimators is more efficient than the usual variance estimator and the class of variance estimators proposed by Singh et al. (2011) when we used more than one auxiliary variable. In addition, we support this with the aid of numerical illustration.     

Mixed sampling plan based on exponentially weighted moving average statistic

ABSTRACT

We will design a new mixed acceptance sampling plan based on the exponentially weighted moving average statistic in this article. The plan parameters of the proposed plan are determined by an optimization problem. The efficiency of the proposed plan is compared with the existing attribute sampling plan. An industrial example is given for illustration purpose.     

Estimation of rare and clustered population variance in adaptive cluster sampling

Abstract

Many researchers used auxiliary information together with survey variable to improve the efficiency of population parameters like mean, variance, total and proportion. Ratio and regression estimation are the most commonly used methods that utilized auxiliary information in different ways to get the maximum benefits in the form of high precision of the estimators. Thompson first introduced the concept of Adaptive cluster sampling, which is an appropriate technique for collecting the samples from rare and clustered populations. In this article, a generalized exponential type estimator is proposed and its properties have been studied for the estimation of rare and highly clustered population variance using single auxiliary information. A numerical study is carried out on a real and artificial population to judge the performance of the proposed estimator over the competing estimators. It is shown that the proposed generalized exponential type estimator is more efficient than the adaptive and non adaptive estimators under conventional sampling design.     

Bayesian estimation for randomly censored generalized exponential distribution under asymmetric loss functions

This paper deals with the Bayesian estimation of generalized exponential distribution in the proportional hazards model of random censorship under asymmetric loss functions. It is well known for the two-parameter lifetime distributions that the continuous conjugate priors for parameters do not exist; we assume independent gamma priors for the scale and the shape parameters. It is observed that the closed-form expressions for the Bayes estimators cannot be obtained; we propose Tierney–Kadane's approximation and Gibbs sampling to approximate the Bayes estimates. Monte Carlo simulation is carried out to observe the behavior of the proposed methods and one real data analysis is performed for illustration. Bayesian methods are compared with maximum likelihood and it is observed that the Bayes estimators perform better than the maximum-likelihood estimators in some cases.     

Analysis of survival data by a Weibull–Bessel distribution

In survival analysis and reliability studies, problems with random sample size arise quite frequently. More specifically, in cancer studies, the number of clonogens is unknown and the time to relapse of the cancer is defined by the minimum of the incubation times of the various clonogenic cells. In this article, we have proposed a new model where the distribution of the incubation time is taken as Weibull and the distribution of the random sample size as Bessel, giving rise to a Weibull–Bessel distribution. The maximum likelihood estimation of the model parameters is studied and a score test is developed to compare it with its special submodel, namely, exponential–Bessel distribution. To illustrate the model, two real datasets are examined, and it is shown that the proposed model, presented here, fits better than several other existing models in the literature. Extensive simulation studies are also carried out to examine the performance of the estimates.