Aplicação de Algoritmos de Machine Learning para Otimização Volumétrica e Consolidação de Cargas em Contêineres de Importação para os EUA

The growing logistical complexity in international trade demands increasingly sophisticated solutions to achieve efficient resource utilization and cost reduction. In this context, the application of Machine Learning (ML) algorithms in volumetric optimization and cargo consolidation in import containers to the United States represents an innovation with transformative potential. This article investigates how supervised and unsupervised learning techniques can be applied to predict occupancy patterns, improve space utilization, and minimize logistical waste. Furthermore, it discusses the integration of these technologies into transportation management platforms, analyzing their impact on business competitiveness, sustainability, and the reduction of operational bottlenecks.