Development of Models Predicting Dwell Time of Import Containers in Port Container Terminals – An Artificial Neural Networks Application

The general aim of this paper is to propose the development of a methodological framework that incorporates the various factors affecting the Dwell Time (DT) of containers in container terminals. Container terminals are regarded as a key element of logistics chains since they are a link between sea and the hinterland transportation modes. Workload forecasting is essential when it comes to truck arrivals for the avoidance of bottlenecks and the smooth integration of container terminals in the supply chain. Terminal operators, tend to make stacking decisions based on mostly on factors suc the container's weight, size and type. The suggested methodology requires the collection of aggregate data and the application of Artificial Neural Networks (ANN) to identify the determinants of Dwell Time (DT). The first results of the ANN showed that the most important factors affecting significantly the model's accuracy are the following: containers size and type, the day and month of the container's discharge, the vessel's port of origin and the commodities transported.