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Ports are essential nodes in global trade, linking maritime and land transport. As maritime logistics increasingly drive global supply chains, optimizing port operations has become vital for enhancing economic efficiency and environmental sustainability. This study presents a Mixed Integer Linear Programming (MILP) model to address inefficiencies in berth allocation and stevedoring processes at the Port of Leixões, Portugal. By integrating real operational data, the model reduces vessel waiting times by 47.56% (from 8.1 to 4.2 h) and operational delays by 37.39% (from 11.5 to 7.2 h). These optimizations also result in a 41.85% reduction in greenhouse gas emissions per ship, aligning with global emissions regulations and promoting sustainable port management. The model’s innovations include real-time data integration and a holistic resource allocation approach to mitigate congestion and inefficiencies. Key findings demonstrate the model’s potential to streamline operations and minimize environmental impacts. These advancements align economic efficiency with environmental sustainability, addressing global emissions regulations. However, the study acknowledges limitations, such as excluding unpredictable factors like weather conditions and equipment failures. Future research should explore dynamic variables, such as weather conditions and mechanical failures, and expand the model’s applicability to other seaports worldwide.

Ports are essential nodes in global trade, linking maritime and land transport. As maritime logistics increasingly drive global supply chains, optimizing port operations has become vital for enhancing economic efficiency and environmental sustainability. This study presents a Mixed Integer Linear Programming (MILP) model to address inefficiencies in berth allocation and stevedoring processes at the Port of Leixões, Portugal. By integrating real operational data, the model reduces vessel waiting times by 47.56% (from 8.1 to 4.2 h) and operational delays by 37.39% (from 11.5 to 7.2 h). These optimizations also result in a 41.85% reduction in greenhouse gas emissions per ship, aligning with global emissions regulations and promoting sustainable port management. The model's innovations include real-time data integration and a holistic resource allocation approach to mitigate congestion and inefficiencies. Key findings demonstrate the model's potential to streamline operations and minimize environmental impacts. These advancements align economic efficiency with environmental sustainability, addressing global emissions regulations. However, the study acknowledges limitations, such as excluding unpredictable factors like weather conditions and equipment failures. Future research should explore dynamic variables, such as weather conditions and mechanical failures, and expand the model's applicability to other seaports worldwide. [ABSTRACT FROM AUTHOR]