Ugljesa Bugaric

Optimal utilization of the terminal for bulk cargo unloading

This paper analyses the capacity of a bulk cargo unloading river terminal, i.e. the existing terminal configuration with two unloading devices (operation without a help strategy and under a complete help strategy between unloading devices) and the predicted future terminal configuration with three unloading devices operating under a partial help strategy. The optimization procedure to determine unloading terminal optimal utilization, for each terminal configuration and operation strategy (existing and future situation) is also shown, due to the fact that ports (terminals) operation under an optimal capacity provides prompt accommodation of vessels with the minimum port waiting time and maximum use of berth facilities. For the purpose of a comprehensive analysis, three different river terminal simulation models have been developed due to the number of unloading devices and their operation strategy. Some of the obtained results have been applied and verified on the existing system.

Enrollment Management Model: Artificial Neural Networks versus Logistic Regression

ABSTRACT This paper presents an enrollment management model by applying artificial neural network (ANN). The aim of the research, which has been presented in this paper, is to show that ANNs are more successful in predicting than the classical statistical method – regression analysis (logistic regression). Both predictive models, no matter whether they are based on ANNs or logistic regression, offer satisfactory predictive results, and they can offer support in the decision-making process. However, the model based on neural networks shows certain advantages. ANNs demand understanding of functional connection between independent and dependent variables in order to evaluate the model. Also, they adapt easily to related independent variables, without the appearance of the problem of multicollinearity. In contrast to logistic regression, neural networks can recognize the appearance of nonlinearity and interactions in input data, and they can react on time.