Bi-objective multi-resource scheduling problem for emergency relief operations

Abstract

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.     

Demography,Education, and Democracy: Global Trends and the Case of Iran

Reconstructions and projections of populations by age, sex, and educational attainment for 120 countries since 1970 are used to assess the global relationship between improvements in human capital and democracy. Democracy is measured by the Freedom House indicator of political rights. Similar to an earlier study on the effects of improving educational attainment on economic growth, the greater age detail of this new dataset resolves earlier ambiguities about the effect of improving education as assessed using a global set of national time series. The results show consistently strong effects of improving overall levels of educational attainment, of a narrowing gender gap in education, and of fertility declines and the subsequent changes in age structure on improvements in the democracy indicator. This global relationship is then applied to the Islamic Republic of Iran. Over the past two decades Iran has experienced the world's most rapid fertility decline associated with massive increases in female education. The results show that based on the experience of 120 countries since 1970, Iran has a high chance of significant movement toward more democracy over the following two decades.     

MDEWMA chart: an efficient and robust alternative to monitor process dispersion

Control chart is the most important statistical process control tool used to monitor changes in process location and dispersion. In this study, an EWMA control chart is proposed for efficient and robust monitoring of process dispersion. The proposed chart, namely the MDEWMA chart, is based on estimating the process standard deviation (σ) using the mean absolute deviations (MD), taken from the sample median. The performance of the proposed chart has been compared with the EWMASR chart (a dispersion EWMA chart based on sample range) and MD chart (a Shewhart-type dispersion chart based on MD), under the existence and violation of normality assumption. It has been observed that the proposed MDEWMA chart is more efficient and robust when compared with both EWMASR and MD charts in terms of run length (RL) characteristics such as average RL, median RL and standard deviation of the RL distribution.     

The role of time perspective in predicting Iranian farmers’ participatory-based water conservation attitude and behavior

ABSTRACT

In current perspective, farmers’ participatory behavior toward conservation of water resources (FPBCWR) is one of the most important strategies under water resource management in rural Iran. In this regard, understanding the predictors of farmers’ participatory-based water conservation behaviors and attitudes is gaining more importance than earlier. Among different dimensions of farmers’ participatory behavior, the potential of temporal frames was examined rarely. Thus, 322 Iranian farmers were investigated to examine the potential of their time perspectives in predicting their participatory-based water conservation behavior and attitude. According to the study results, the effects of present orientation on attitude and behavior were negatively significant, while the effects of future orientation on attitude and behavior were positively significant, whereas its effects (path coefficients) were stronger than present orientation effects. Past orientation did not have a significant effect on attitude, though attitude positively and significantly affected farmers’ participatory behavior. The results of causal analysis revealed that presented model accounted for 58% of variance in farmers’ ‘behavior’ and 42% of variance in “attitude.” In conclusion, a few demonstrable illustrations of policy implications are presented to enable utilizing the important findings and concluding results of this study that is linked further with water resource management domain.     

Integrationstandems und Supervised Networking mit geflüchteten Menschen in studentischen Gruppen durch Service LearningIntegration Tandems and Supervised Networking with Refugees in Student Groups through Service Learning

This contribution reports on a pilot project which took place during the winter semester 2015/16 at the University of Göttingen based on the concept of Service Learning. As part of service learning, students assume practical tasks from the social environment and apply academic knowledge. With this new didactical approach, personality development of the students as well as civil and democratic values in society should be enhanced.     

Enhanced adaptive CUSUM charts for process mean

A statistical quality control chart is an important tool of the statistical process control, which is widely used to control and monitor a production process. The CUSUM chart is designed to detect a specific shift, provided that the shift size is known in advance. In practice, however, shift sizes are rarely known. It is then customary to use an adaptive CUSUM chart, which can effectively detect a range of shift sizes. In this paper, we enhance the sensitivities of the improved adaptive CUSUM mean charts using an auxiliary-information-based (AIB) mean estimator. The run length performances of the proposed charts are compared with those of the AIB adaptive and non-adaptive CUSUM charts in terms of the average run length (ARL), extra quadratic loss, and integral relative ARL. These run length comparisons reveal that the proposed charts are more sensitive than the existing charts when detecting different kinds of shift in the process mean. An example is given to demonstrate the implementation of existing and proposed charts.     

Some bounds related to Harris family of distributions

As a lifetime distribution, Harris family of distributions are applied to the lifetime of a series system with random number of components. In this paper, properties of various ageing classes of mixtures of Harris family of distributions, where the tilt parameter of a Harris distribution is taken as a random variable, are studied. We obtain an upper bound for maximum error in evaluating its reliability function. Two bounds are also presented for survival function and expectation of the mixed Harris family. We also provide some interesting bounds for its residual survival function. Our results generalize several previous findings in this connection. Some illustrative examples are also provided.     

On a multivariate Lindley distribution

This article proposes a multivariate extension of the generalized Lindley distribution introduced by Abouammoh et al. (2015 Abouammoh, A.M., Alshangiti Arwa, M., Ragab, I.E. (2015). Reliability estimation of the generalized Lindley distributions. J. Stat. Comput. Simul. 85(17):3662–3678.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). The proposed model is based on a probabilistic construction in which several Lindley random variables are connected by a common shock. Many statistical properties of this new distribution are explored. In particular, explicit forms of product moments, moment-generating function, and conditional moments are derived. Explicit expressions of moment-based estimators of the underlying parameters of the proposed model are established. Estimation using the maximum likelihood procedure is also investigated. Simulations attesting to the quality of the proposed estimators are presented. Application of the proposed model in reliability is also discussed.     

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.     

Process Capability Analysis in Non Normal Linear Regression Profiles

When the distribution of a process characterized by a profile is non normal, process capability analysis using normal assumption often leads to erroneous interpretations of the process performance. Profile monitoring is a relatively new set of techniques in quality control that is used in situations where the state of product or process is represented by a function of two or more quality characteristics. Such profiles can be modeled using linear or nonlinear regression models. In some applications, it is assumed that the quality characteristics follow a normal distribution; however, in certain applications this assumption may fail to hold and may yield misleading results. In this article, we consider process capability analysis of non normal linear profiles. We investigate and compare five methods to estimate non normal process capability index (PCI) in profiles. In three of the methods, an estimation of the cumulative distribution function (cdf) of the process is required to analyze process capability in profiles. In order to estimate cdf of the process, we use a Burr XII distribution as well as empirical distributions. However, the resulted PCI with estimating cdf of the process is sometimes far from its true value. So, here we apply artificial neural network with supervised learning which allows the estimation of PCIs in profiles without the need to estimate cdf of the process. Box-Cox transformation technique is also developed to deal with non normal situations. Finally, a comparison study is performed through the simulation of Gamma, Weibull, Lognormal, Beta and student-t data.