## 4 Results and Discussion
### Design of the Multi-Agent System Architecture (MAS)
#### 4.2.2 Selection of Algorithms and Technologies for Optimization
Each entity requires a specific approach:  
* Heuristic optimization in combinatorial problems. * Efficient route search in dynamic environments. * Data-driven predictive modeling in traffic management and monitoring. * Multi-agent negotiation for decentralized coordination. The rationale for the Core Algorithms is detailed below:
* Efficient and flexible coordination: the use of contract net protocol (CNP)-based trading in the EOO allows agents to manage tasks in a decentralized manner, reducing overhead on a single node and ensuring adaptability to changes in demand. * Rapid optimization in space allocation: the taboo search and genetic algorithms in the EDV allow efficient solutions to be found without the need to evaluate all possible combinations, achieving a better distribution of vehicles in the field with reduced processing times. * Dynamic and efficient routes: the use of A*, Dijkstra, and reinforcement learning in the ENR allows optimal routes to be calculated by adapting to changes in traffic, ensuring shorter and more efficient journeys within the terminal. * Intelligent traffic management: machine learning with predictive models in the ECF allows congestion to be anticipated and traffic to be dynamically adjusted in the terminal, avoiding blockages and downtime in operations. * Accurate boarding synchronization: the application of metaheuristics for dynamic scheduling in the ECE improves the allocation of boarding shifts, reducing delays and optimizing ramp utilization. * Monitoring and continuous improvement: the use of machine learning to detect anomalies in ESO allows for identifying patterns of inefficiency and optimizing the system based on historical data, ensuring progressive improvements in port management. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Entity** & **Main Algorithm** & **Justification** \\ \hline ENR—Navigation and Routes Entity & Pathfinder A, Dijkstra, reinforcement learning & To find the shortest path from the field to the boarding ramp, route planning algorithms are required. Dijkstra ensures the optimal path in networks with positive weights, while A* improves efficiency in dynamic environments with heuristics. If traffic is highly changeable, you can use reinforcement learning, which adapts routing decisions based on previous experiences. \\ \hline ECF—Flow Control Entity & Traffic models with IoT and machine learning & Real-time traffic management involves handling sensor data and predicting congestion patterns. Machine Learning models (regression, neural networks) can anticipate areas of high congestion and suggest proactive adjustments in traffic flows, avoiding bottlenecks. \\ \hline ECE—Shipping Coordination Entity & Dynamic scheduling with metaheuristics & Vehicle coordination on the boarding ramp must be adapted based on the load, vessel restrictions, and ramp availability. Algorithms such as GRASP (Greedy Randomized Adaptive Search Procedure) and Colony Optimization optimize shipment time planning with computational efficiency. \\ \hline ESO—Supervisory and Optimization Entity & Machine learning for anomaly detection and optimization optimization & To continuously evaluate the efficiency of the system, it is necessary to analyze historical patterns and detect anomalies in operations. Unsupervised learning models (clustering, outlier detection with DBSCAN or Isolation Forest) allow inefficiencies to be identified without manual intervention. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._These algorithms ensure that the multi-agent system (MAS) operates with efficiency and scalability, enabling autonomous and optimized decision-making at the Ro-Ro terminal.