An Efficient and Robust Design for Transshipment Networks

Transshipment, the sharing of inventory among parties at the same echelon level of a supply chain, can be used to reduce costs. The effectiveness of transshipment is in part determined by the configuration of the transshipment network. We introduce chain configurations in transshipment settings, where every party is linked in one connected loop. Under simplifying assumptions we show analytically that the chain configuration is superior to configurations suggested in the literature. In addition, we demonstrate the efficiency and robustness of chain configurations for more general scenarios and provide managerial insights regarding preferred configurations for different problem parameters.     

The minimum value of geometric-arithmetic index of graphs with minimum degree 2

The geometric-arithmetic index was introduced in the chemical graph theory and it has shown to be applicable. The aim of this paper is to obtain the extremal graphs with respect to the geometric-arithmetic index among all graphs with minimum degree 2. Let G(2, n) be the set of connected simple graphs on n vertices with minimum degree 2. We use linear programming formulation and prove that the minimum value of the first geometric-arithmetic \((GA_{1})\) index of G(2, n) is obtained by the following formula:

$$\begin{aligned} GA_1^* = \left\{ \begin{array}{ll} n&{}\quad n \le 24, \\ \mathrm{{24}}\mathrm{{.79}}&{}\quad n = 25, \\ \frac{{4\left( {n - 2} \right) \sqrt{2\left( {n - 2} \right) } }}{n}&{}\quad n \ge 26. \\ \end{array} \right. \end{aligned}$$

     

A Desire to Return to the Country of Birth as a Function of Language Preference: An Empirical Study with Iranian Immigrants in the United States

It was hypothesized that bilingual Iranian immigrants in the United States who prefer to use the Persian language, compared to Iranians who prefer to use English, would be more likely to express a desire to return to Iran to live. Participants were 292 bilingual Iranian immigrants in the United States (128 men, 164 women) who completed a questionnaire with a similar content, prepared in Persian on one side and in English on the other side. Participants were given the option to either complete the Persian or English version of the questionnaire. Results confirmed the research hypothesis suggesting that the immigrants’ preference in using their native language serves as a “pull” factor that increases the probability of a desire to return to the country of birth. It was also found that women and younger respondents were less likely to express a desire to return to Iran. Level of education, marital status, years of living in the United States, and attitudes toward marriage and the family did not predict such a desire. Implications for acculturation research and for counseling culturally diverse clients are discussed. Policy implications to attract immigrants back to their country of birth were also discussed.     

An Insurance Model for Risk Management of Process Facilities

Most existing risk management models for process industries do not consider the effect of insurance coverage, which results in an overestimation of overall risk. A model is presented in this article to study the effect of insurance coverage of health, safety, environmental, and business risks. The effect of insurance recovery is modeled through the application of adjustment factors by considering the stochastic factors affecting insurance recovery. The insurance contract's conditions, deductibles, and policy limits are considered in developing the insurance recovery adjustment factors. Copula functions and Monte Carlo simulations are used to develop the distribution of the aggregate loss by considering the dependence among loss classes. A case study is used to demonstrate both the practical application of the proposed insurance model to improve management decisions, and the mitigating effect of insurance to minimize the residual risk.     

Women’s Well-Being in Iran: Territorial Analysis Using a Multidimensional Approach

This paper employs a multidimensional index for assessing women’s well-being in Iranian provinces using demographic and health survey macro data. Besides economic status as a traditional dimension for objective well-being, other factors such as health, education, risk, and technology as well as self-reported life satisfaction and happiness (psychological well-being) have been included as non-material well-being dimensions. We found that the values of women’s well-being are distributed unequally across the provinces. Southern provinces demonstrate the worst performances and central provinces have the best. Generally, Iranian women are in poor condition in terms of economic variables (e.g. income and participation in the job market), psychological well-being (life satisfaction), HIV literacy, use of the internet, and access to mass media. On the other hand, in recent years, they have made significant progress in education (such as the rate of literacy, high school degree), and health (safe pregnancy care and nutritional supplements).     

Bayesian analysis of covariance under inverse Gaussian model

This paper considers the problem of analysis of covariance (ANCOVA) under the assumption of inverse Gaussian distribution for response variable from the Bayesian point of view. We develop a fully Bayesian model for ANCOVA based on the conjugate prior distributions for parameters contained in the model. The Bayes estimator of parameters, ANCOVA model and adjusted effects for both treatments and covariates along with predictive distribution of future observations are developed. We also provide the essentials for comparing adjusted treatments effects and adjusted factor effects. A simulation study and a real world application are also performed to illustrate and evaluate the proposed Bayesian model.     

Monitoring Multi-Attribute Processes Based on NORTA Inverse Transformed Vectors

Although multivariate statistical process control has been receiving a well-deserved attention in the literature, little work has been done to deal with multi-attribute processes. While by the NORTA algorithm one can generate an arbitrary multi-dimensional random vector by transforming a multi-dimensional standard normal vector, in this article, using inverse transformation method, we initially transform a multi-attribute random vector so that the marginal probability distributions associated with the transformed random variables are approximately normal. Then, we estimate the covariance matrix of the transformed vector via simulation. Finally, we apply the well-known T ² control chart to the transformed vector. We use some simulation experiments to illustrate the proposed method and to compare its performance with that of the deleted-Y method. The results show that the proposed method works better than the deleted-Y method in terms of the out-of-control average run length criterion.     

New Statistic to Increase Correctness in Simulation Factor Screening Using Frequency Domain Method

Simulation models often include a large number of input factors, many of them may be unimportant to the output; justifying the use of factor screening experiments to eliminate unimportant input factors from consideration in later stages of analysis. With a large number of factors, the challenge is designing experiments so that total number of runs and consequently the required time and cost decrease while achieving a satisfactory detection rate. This article employs frequency domain method (FDM) which is applicable in discrete-event simulation models to propose a new statistic defined as the ratio of estimated signal spectrum to maximum estimated noise spectrum. The proposed method not only has the FDM advantages compared to classic screening approaches but also helps to reduce the error of associated with distinguishing important effects from unimportant ones. Furthermore, as an alternative to the existing statistics, it is shown that not only the proposed statistic does not deteriorate the power of the screening test but in some instances it helps to improve it.     

On a Selection Weibull Distribution

In this article, a selection Weibull distribution is investigated. First, some properties and representations of the model with some plots of the density and hazard rate functions are illustrated. Second, some simple relations of this model with some distributions discussed. In addition, maximum likelihood estimators obtained with numerical methods, and compared by three sub-models with a data set that shows the performance of our model. Finally, a simulation study presented for all parameters.