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In a container terminal task assignment optimization may not be sufficient to ensure efficiency. By contrast, a systemic approach to terminal operations planning is required to ensure efficient coordination day by day of the all the operations in-volved. As far as management is concerned, a number of interacting factors need to be taken into account, ranging from prediction of ships’ arrivals to manpower and vehicles optimization strategies for the different work shifts. This paper describes three modules comprising a Decision Support System designed to assist port operations planners: - Ships’ arrivals forecasting module; - Optimal human resources allocation module; - Maintenance process engineering module. First tests indicate that this integrated tool makes flexible and prompt planning of overall operations possible, thereby reducing costs and enhancing their efficiency and thus competitiveness of the container terminal.

The purpose of the work described in this paper is to construct and implement prediction models for optimizing container handling in particular at Cagliari’s Terminal Container. Prediction models are based on heuristic algorithms such as neural networks and classification and regression trees and evolutionary algorithms such as Genetic Algorithms (GA) and Ant Colony Optimization (ACO). These models form part of a Web Oriented Decision Support System, for real time external data acquisition (GPS information, weather information, etc.), providing operators with the information processed in real time. The most commonly used parameter for assessing terminal performance is productivity, namely the number of containers handled in the unit of time considered. This parameter can be associated with the terminal as a whole, or with the ship, the stevedores, each vehicle used, the single operator and related to different time intervals (year, month, week, day, hour and shift). Usually the hourly average is considered for monitoring operations and identifying shortcomings. The rate at which operations are performed can significantly reduce turn round time and thus minimize the loss of productivity associated with the ship’s time in port. Because of the complexity of analyzing decision-making processes two sublevels are defined, that differ for type of decision and time horizon: - The first level, generally organized around a weekly time horizon (from 10 to 1 days prior to ship’s arrival in port), for scheduling operations and activities in the different areas such as, ship, quay, yard, for making decisions that satisfy the different requirements;- The second level, aimed at specific resource allocation (personnel and equipment) on the basis of the decisions made at the first planning level in order to maximise productivity and minimize costs over a time horizon of roughly 24h. Both levels of planning are characterized by temporal fragmentation and uncertain information. The information is received at undefined times and is continually updated, resulting in uncertain content. The strong dependence of the planning process on information flow, means it is necessarily dynamic and makes it difficult to effectively optimize and integrate decisions over sufficiently broad time horizons. The aim of the study is to construct a model for predicting containership arrivals using heuristic-based evolutionary algorithms. The so-called ―Inspect Inspired Algorithms‖ are proving effective tools for solving industrial optimization issues. In this study the different models proposed are implemented in a ―Decision Support System‖, while data are analysed from a temporal aspect adopting a ―learning from data‖ approach. Indeed the observation of real data (actual arrival time of ships and handling in port) form the knowledge base which relies on learning from the past. All discrepancies observed between prediction models and reality, along with other factors governing that condition prediction errors, create a historical base on which models are automatically recalibrated. This approach has the dual purpose of analysing the causes (shortcomings) of prediction errors while refining models for future prediction; an analysis of the causes and effects of recalibrating the models. The proposed DSS can also be used for simulation purposes. In fact the algorithms will be implemented for studying the effects of external variables taken individually or interacting with one another, thus providing a useful planning tool.

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