This work tackles inefficiencies in outbound train loading at dry ports, focusing on an advanced variation, with new objectives identified by Fordesi and MEDWAY terminal in the context of project Nexus, of the Wagon-Container Assignment Problem (WCAP). Inefficiencies include the failure to load high-priority containers—compromising urgent deadlines and terminal competitiveness—and the underutilization of train capacity, leading to container congestion. Additionally, inefficient reach stacker paths increase travel distances, raising fuel costs, equipment maintenance, and greenhouse gas emissions. The increasing volume of containerized freight highlights the need for optimized cargo handling. To address these challenges, we introduce a novel chromosome representation and fitness function for the Three-Part Genetic Algorithm 2 (TPGA2). Moreover, we further enhance our approach by integrating TPGA2 with the$\epsilon $-constraint method to generate a set of non-dominated solutions. This approach prioritizes high-priority containers, maximizes train capacity, and minimizes travel distances within a constrained solution space. The search is restricted to solutions that either fully or nearly fully load the trains while ensuring high-priority containers are loaded first. Tested across three scenarios of increasing complexity, TPGA2 consistently outperformed heuristic methods, especially in the most realistic scenario, meeting critical deadlines and maximizing train utilization by at least 98.6%. Moreover, the TPGA2 set of non-dominated solutions outperformed all heuristic approaches. Lastly, by aligning with train loading staff priorities and ensuring the most urgent deadlines were met first, TPGA2 reduced loading distances by 7%, leading to an estimated annual savings of 9,049.41e.

Machine learning is an area of Artificial Intelligence in which applying algorithms to a dataset makes it possible to predict results or even discover relationships that would be unnoticeable at first glance. Currently the amount of information available in different areas, especially in health care where data collection and analysis seek to define personalized medicine strategies. This is a field where using Machine Learning-based tools can assume a relevant role. This work presents a study of diverse classification algorithms in the area of machine learning applied to identification of amino acid profiles. The authors defined as a major objective to develop a new biomarker profile for prediction and prognosis of rectal cancer. The data involved in the training of classification algorithms refer to patients with metabolic diseases and rectal cancer. The best machine learning classification models will be tested to achieve the most effective decision support system for a most adequate treatment option selection in order to reduce the morbidity and mortality rate.