## 5 Conclusions  
The implementation of a multi-agent system in the management of Ro-Ro terminals represents a significant advance in the digitalization and optimization of port operations. Throughout this study, a model has been developed that allows for the dynamic allocation of spaces, the optimization of internal routes, and the proactive management of traffic and boarding. Unlike traditional approaches, the proposed system integrates artificial intelligence and advanced heuristic algorithms to improve operational efficiency and reduce transit times within the terminal. The comparative analysis carried out in this study has shown that the digitalization of these processes can generate tangible improvements in port operations. It has been identified that the implementation of intelligent agents can reduce the time it takes to occupy spaces in the field by up to 25%, minimize unnecessary trips through the use of dynamic routes, and improve the ability of internal traffic to adapt to variations in operational demand. In addition, intelligent boarding synchronization reduces waiting times and optimizes ramp capacity, contributing to reduced costs and increased terminal productivity. From a technological perspective, the results obtained confirm the feasibility of using multi-agent systems in port environments. The integration of algorithms such as A*, Dijkstra, and reinforcement learning for route optimization, heuristic models for space allocation, and multi-agent negotiation strategies such as contract net protocol (CNP), have proven to be effective tools in decentralized decision-making. These advances position artificial intelligence as a key factor in the evolution toward more efficient and sustainable ports. Despite the benefits identified, the implementation of these systems in real environments presents challenges. Interoperability with existing port management systems, the need for appropriate technological infrastructure, and resistance to change on the part of port operators are factors that need to be considered. In addition, human supervision remains a critical element in ensuring safety and strategic decision-making in critical operational situations. As a future line of research, it is recommended that experimental tests and simulations be carried out to more accurately quantify the benefits of the proposed system. Validation in real environments would allow the impact of the model to be evaluated in different operational scenarios and to define scalable implementation strategies. Likewise, the use of hybrid models that combine AI with historical data analysis could further improve the predictive capacity of the system. In conclusion, this study demonstrates that the use of artificial intelligence and multi-agent systems in Ro-Ro terminals is a viable and promising alternative to optimize port management. The application of these technologies not only improves operational efficiency but also contributes to the transition to more automated, resilient ports aligned with Industry 4.0 principles. Although the model has not yet been validated through real-world implementation, it offers a solid conceptual basis for future experimental research, simulation studies, and pilot projects within operational Ro-Ro terminals. While this paper does not include performance data from simulations or real deployments, the proposed model offers a structured foundation for future experimentation. The next stage of research will involve testing the system in controlled environments, either through discrete-event simulation or collaborative pilot implementations in operational terminals. It is important to note that the system remains a conceptual model and has not yet been validated through simulation or real-world implementation. Its contribution lies in providing a structured foundation for future experimentation and applied development. Conceptualization, N.G.-C. and A.C.-O.