Diseño de un modelo de aprendizaje profundo para automatizar la entrada y salida de contenedores en la Terminal de Contenedores de Cartagena (CONTECAR
DOI:
https://doi.org/10.18041/2619-4244/dl.36.12539Keywords:
Terminal portuaria, Aprendizaje profundo, Inteligencia artificial, OCR, RFID, Automatización logística, Seguimiento de contenedoresAbstract
En las operaciones portuarias, la automatización de procesos —como la identificación de contenedores mediante tecnologías de Reconocimiento Óptico de Caracteres (OCR) y de Identificación por Radiofrecuencia (RFID)— es fundamental para agilizar los flujos de trabajo y reducir el error humano. La implementación de estos sistemas no solo incrementa la competitividad y la seguridad, sino que también minimiza los riesgos operativos, mejora la agilidad en la gestión de los flujos logísticos y asegura la capacidad de adaptación ante la creciente demanda, contribuyendo así a la sostenibilidad de las infraestructuras portuarias.
La presente investigación de enfoque mixto tiene como objetivo diseñar un modelo de aprendizaje profundo para optimizar los procesos de entrada y salida de contenedores en la Terminal de Contenedores de Cartagena (CONTECAR). El modelo se desarrollará considerando la infraestructura existente, las tecnologías implementadas hasta la fecha, la maquinaria disponible y las características logísticas y organizativas de los procesos operativos de la terminal.
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