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Featured Application: Knowing the departure date of each container is paramount to scheduling an optimal stacking in container terminals, and thus, reducing the fuel consumption of the yard cranes. Supervised classification algorithms are typical for estimating such a dwell time. However, we show that an ordinal regression algorithm outperforms the supervised classification algorithms regarding the mean absolute error and the reshuffles generated. This research has been applied in an inbound yard from Cuba as part of a project for the optimization of the import container flow. Our results can state a baseline for further dwell time estimation systems. The optimal stacking of import containers in a terminal reduces the reshuffles during the unloading operations. Knowing the departure date of each container is critical for optimal stacking. However, such a date is rarely known because it depends on various attributes. Therefore, some authors have proposed estimation algorithms using supervised classification. Although supervised classifiers can estimate this dwell time, the variable "dwell time" takes ordered values for this problem, suggesting using ordinal regression algorithms. Thus, we have compared an ordinal regression algorithm (selected from 15) against two supervised classifiers (selected from 30). We have set up two datasets with data collected in a container terminal. We have extracted and evaluated 35 attributes related to the dwell time. Additionally, we have run 21 experiments to evaluate both approaches regarding the mean absolute error modified and the reshuffles. As a result, we have found that the ordinal regression algorithm outperforms the supervised classifiers, reaching the lowest mean absolute error modified in 15 ( 71 % ) and the lowest reshuffles in 14 ( 67 % ) experiments. [ABSTRACT FROM AUTHOR]