In recent years, despite a decline in international trade and disruptions in the supply chain caused by COVID-19, the main container terminals in Latin America and the Caribbean (LAC) have increased their container volumes. This growth has necessitated significant adaptations by seaports and their authorities to meet new demands. Consequently, there has been a focused analysis on the performance, efficiency, and competitiveness, particularly their most relevant logistical aspects. In this paper, a multi-objective hybrid approach was employed. The Principal Component Analysis (PCA) technique was combined with the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to rank LAC container terminals and identify operational criteria affecting efficiency. The analysis considered all input variables (berth/quay length, quay draught, yard area, number of quay cranes (portainer), number of yard cranes (trastainer), reachstacker, multicranes, daily montainer movement capacity, number of station reefer container type, number of terminals, and distance to the Panama Canal) and output variable (port performance expressed in TEUs from 2014 to 2023). The results revealed noteworthy findings for several terminals, particularly Colón, Santos, or Cartagena, which stands out as the main container port in LAC not only in annual TEUs throughput, but also in resource utilization.

The Consorzio Napoletano Terminal Containers (CO.NA.TE.CO.), located in the Port of Naples, is continuously exposed to a problem of traffic congestion. A queuing model has been developed to analyze this congestion problem. The model includes part of the process of exportation. The entities represented the vehicles that access into the terminal, and the servers were the entrance gate and the process of moving the freight to the yard. According to these definitions, five models have been developed with the aim of simulating solutions to the congestion problem. The first model reproduces the current situation, and the modifications in the servers have been evaluated through the rest. Finally, it has been proved that all processes inside the terminal were strongly dependent. The study shows that the solution should consider the decreasing simultaneously the time of service in the access gate and in the yard

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.