The paper outline the evolution of container terminal requirements for simulation and the potential of new advanced techniques in the integration with these aspects; the authors present application examples and experimental results that maximized the impact of these new concepts in real complex port realities.

This paper aims to examine how the radial basis function (RBF) technique works in the financial field, to compare the RBF performance with the results obtained with traditional methods (FDM, FEM), to choose the more suitable radial basis function to solve option pricing and to explain how its shape parameters can be set. It is crucial to set properly the shape parameter for the precision of the method and ultimately for the determination of the derivatives fair-value. Applying a maximum likelihood estimation (MLE), the authors propose a financial approach for its evaluation based on market/theoretical prices calibration.

The global financial crisis of 2007-2008 has highlighted the importance of a correct pricing of the so-called financial derivatives. Analyzing the methodology of pricing of non-listed derivatives by using the Monte 6168Ilaria Bendato et al.Carlo method, the Authors have realized that the determination of the sample size is not managed properly. This is because the research offices of banks rely on, as suggested by the literature of the field and technical manuals for practitioners, a standard number of simulation runs, by rules of thumb, between 1,000 and 10,000. The consequence is that financial institutions lead to financial statements fair values with no knowledge of its fluctuation band and the robustness of the result. Conscious of this practice, the Authors, dealing from a long time to the topic of output reliability in applications of discrete event simulation and Monte Carlo simulation, address the problem through the use of a methodology based on the control of Mean Pure Square Error (MSPE), already successfully tested in other contexts. Thanks to the proposed approach, applied for pricing complex derivatives, it is possible to determine the size of the experimental sample in order to ensure a pre-assigned degree of reliability of the output results.

This work is part of an Italian National Research Project embracing different aspects of a short life-cycle products supply chains: its modeling, its resiliency and its competitiveness. In fact, this particular kind of products, like fashion goods, toys or electronic devices, have different characteristics compared with long-medium life cycle products and this implies a quite different management as well as competitiveness factors to take into account. Starting from the modeling of a supply chain of this kind, utilizing the Powersim Studio Software implementing the System Dynamics methodology, with the goal of showing its behavior under specific scenarios, some vulnerability causes have been considered in order to make the supply chain more resilient. Finally, the competitiveness dynamics between two companies producing short life cycle items has been modeled and analyzed.

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