Time analysis of the containerized cargo flow in the logistic chain using simulation tools: the case of the Port of Seville (Spain)

[EN] The logistic chain that connects the capital of Spain (Madrid) with the Canary Islands has the Port of Seville as the port node. This port node makes possible to switch from one transport mode (railway) to another (maritime) at the container terminal of the port. Some constraints, such as the operational time window that restricts the freight train access into the port in a certain time-slot or the need of the reversal of the train before entering into port, lead to generate important time delays in the intermodal chain. A time analysis of the process is necessary in order to check the critical points. A simulation of the whole process from the goods departing the origin station by train until they leave the port of Seville by ship to the Canary Islands is performed. To this aim, a queuing model network was developed in order to simulate the travel time of the cargo. The database is composed of daily departures of goods train and daily departures of vessels (including times of docking, berthing or load/unload cargo). The final objective of this work is twofold: firstly, to provide a validated model of the containerized cargo flow and secondly, to demonstrate that this kind of queuing models can become a powerful supporting tool in making decisions about future investments.Ruiz Aguilar, J.; Turias, I.; Cerbán, M.; Jiménez Come, MJ.; González, M.; Pulido, Á. (2016). Time analysis of the containerized cargo flow in the logistic chain using simulation tools: the case of the Port of Seville (Spain). En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 1509-1517. https://doi.org/10.4995/CIT2016.2015.3083OCS1509151

Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting

In this paper, the number of goods subject to inspection at European Border Inspections Post are predicted using a hybrid two-step procedure. A hybridization methodology based on integrating the data obtained from autoregressive integrated moving averages (SARIMA) model in the artificial neural network model (ANN) to predict the number of inspections is proposed. Several hybrid approaches are compared and the results indicate that the hybrid models outperform either of the models used separately. This methodology may become a powerful decision-making tool at other inspection facilities of international seaports or airports

Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain)

The prediction of freight congestion (cargo peaks) is an important tool for decision making and it is this paper’s main object of study. Forecasting freight flows can be a useful tool for the whole logistics chain. In this work, a complete methodology is presented in order to obtain the best model to predict freight congestion situations at ports. The prediction is modeled as a classification problem and different approaches are tested (k-Nearest Neighbors, Bayes classifier and Artificial Neural Networks). A panel of different experts (post–hoc methods of Friedman test) has been developed in order to select the best model. The proposed methodology is applied in the Strait of Gibraltar’s logistics hub with a study case being undertaken in Port of Algeciras Bay. The results obtained reveal the efficiency of the presented models that can be applied to improve daily operations planning