Increasing competition in the container shipping sector has meant that\ud terminals are having to equip themselves with increasingly accurate analytical and\ud governance tools. A transhipment terminal is an extremely complex system in terms of both\ud organisation and management. Added to the uncertainty surrounding ships’ arrival time in\ud port and the costs resulting from over-underestimation of resources is the large number of\ud constraints and variables involved in port activities. Predicting ships delays in advance\ud means that the relative demand for each shift can be determined with greater accuracy, and\ud the basic resources then allocated to satisfy that demand. To this end, in this article we\ud propose two algorithms: a dynamic learning predictive algorithm based on neural networks\ud and an optimisation algorithm for resource allocation. The use of these two algorithms\ud permits on the one hand to reduce the uncertainty interval surrounding ships’ arrival in\ud port, ensuring that human resources can be planned around just two shifts. On the other\ud hand, operators can be optimally allocated for the entire workday, taking into account\ud actual demand and operations of the terminal. Moreover, as these algorithms are based on\ud general variables they can be applied to any transhipment terminal. Future integration of\ud the two models within a broader decision support system will provide an important support\ud tool for planners for fast, flexible planning of the terminal’s operations management

The great potential of the Mediterranean area, as yet not fully tapped due to the lack of the integrated management of its ports, calls for innovative management policies for achieving competitiveness within the Mediterranean port system. To this end, the current regime of intra-port competition has proven highly unproductive and needs to be rethought, implementing new cooperation policies. The aim of this study is to identify, by means of traditional clustering techniques, homogeneous groups of ports within the Mediterranean region. In so doing, it would be possible to propose new cooperation policies between ports of the same cluster, but also between different clusters, on the basis of their specific features. A data set has been created for 34 major Mediterranean container ports. Relations between ports have been evaluated from a quantitative perspective through traditional statistical techniques: hierarchical cluster analysis based on the Ward method. Different sets of homogeneous ports have been obtained alternating different combinations of input variables and varying these over suitable ranges, in line with the assumed cooperation policies. The findings provide the basis for exploring the strategic functional relationships among ports, in order to promote collective integrated actions that could prove essential for the competitiveness of the Mediterranean port system.

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One of the most important issues in Transhipment Container Terminal (TCT) management is to have fairly reliable and affordable predictions about vessel arrival. Terminal operators need to estimate the actual time of arrival in port in order to determine the daily demand for each work shift with greater accuracy. In this way, the resources required (human resources, equipment as well as spatial resources) can be allocated more efficiently. Despite contractual obligations to notify the Estimated Time of Arrival (ETA) 24 hours before arrival, ship operators often have to revise it due to unexpected events like weather conditions, delay in a previous port and so on. For planners the decision-making processes related to this topic can sometimes be so complex without the support of suitable methodological tools. Specific models should be adopted, in a daily planning scenario, to provide a useful support tool in TCTs. In this study, we discuss an exploratory analysis of the data affecting delays registered at a Mediterranean TCT. We present some preliminary results obtained using data mining techniques and propose a Classification and Regression Trees (CART) model to reduce the range of uncertainty of ship arrivals in port. This approach is compulsory to manage vast amounts of unstructured data involved in estimating of vessel arrivals. Reference to this paper should be made as follows: Pani, C.; Fadda, P.; Fancello, G.; Frigau, L.; Mola, F. 2014. A data mining approach to forecast late arrivals in a transhipment container terminal, Transport 29(2): 175-184. http://dx.

One of the main issues in the event of a major industrial disaster (fire, explosion or toxic gas dispersion) is to efficacy manage emergencies by considering both medical and logistics issues. From a logistics point of view the purpose of this work is to correctly address critical patients from the emergency site to the most suitable hospitals. A Mixed Integer Programming (MIP) Model is proposed, able to determine the optimal number and allocation of emergency vehicles involved in relief operations, in order to maximize the number of successfully treated injured patients. Moreover, a vehicles reallocation strategy has been developed which takes into account the evolution of the patients health conditions. Alternative scenarios have been tested considering a dynamic version of the Emergency Vehicles Allocation Problem, in which patient health conditions evolves during the rescue process. A company located in Italy has been considered as case-study in order to evaluate the performance of the proposed methodology