## 4 Results and Discussion
### Design of the Multi-Agent System Architecture (MAS)
#### 4.2.2 Selection of Algorithms and Technologies for Optimization
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Advantages** & **Disadvantages** & **Application in the Model** \\ \hline A\({}^{\star}\) (Route Search) & Find the optimal route with computational efficiency. Adaptable to real-time traffic changes. & It can become computationally expensive in environments with many variables. & Optimization of routes within the terminal. \\ \hline Dijkstra (Route Search) & Exact algorithm to find the shortest route. Robust and widely validated. & Less efficient compared to A\({}^{\star}\) in environments with large volumes of data. & Alternative for trajectory planning. \\ \hline Reinforcement Learning Techniques & Able to learn dynamically and improve decisions with experience. & It requires prior training and large volumes of data to improve its accuracy. & Optimization of internal traffic management. \\ \hline Contract Net Protocol (CNP) & It provides a decentralized mechanism for trading between agents. & It can generate more communication traffic between agents, affecting latency in large systems. & Coordination of tasks between agents (spaces, routes, traffic). \\ \hline Taboo Search (Allocation of Spaces) & Find optimal solutions to complex combinatorial problems. & It does not guarantee finding the best global solution, but it avoids falling into local lows. & Optimization of the allocation of spaces in the field. \\ \hline Genetic Algorithms (Allocation Of Spaces) & Ability to optimize in large search spaces. Find solutions close to the optimum. & It requires longer processing time and parameter adjustment for optimal performance. & Alternative for the allocation of vehicle spaces in the terminal. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of algorithms in the port context. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Entity** & **Main Algorithm** & **Justification** \\ \hline EOO—Operations & Multi-agent systems with contract net protocol (CNP)-based trading & An efficient coordination mechanism is required between entities with different objectives and constraints. The contract net protocol (CNP) allows specialized agents to negotiate tasks without overloading a single central node. In addition, it guarantees flexibility in the face of changes in demand and distribution of resources. \\ \hline EDV—Vehicle Distribution Entity & Heuristic optimization (taboo search, genetic algorithms) & The optimal allocation of spaces in the field is a problem of combinatorial optimization. Advanced heuristics, such as taboo search and genetic algorithms, make it possible to find solutions close to the optimal without the need to evaluate all possible combinations, significantly reducing calculation time. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Entity** & **Main Algorithm** & **Justification** \\ \hline EOO—Operations & Multi-agent systems with contract net protocol (CNP)-based trading & An efficient coordination mechanism is required between entities with different objectives and constraints. The contract net protocol (CNP) allows specialized agents to negotiate tasks without overloading a single central node. In addition, it guarantees flexibility in the face of changes in demand and distribution of resources. \\ \hline EDV—Vehicle Distribution Entity & Heuristic optimization (taboo search, genetic algorithms) & The optimal allocation of spaces in the field is a problem of combinatorial optimization. Advanced heuristics, such as taboo search and genetic algorithms, make it possible to find solutions close to the optimal without the need to evaluate all possible combinations, significantly reducing calculation time.  
\end{table} 
Table 3: Justification of the main algorithms by intelligent entity. The selection of these algorithms responds to the need to balance accuracy, computational efficiency, and adaptability in real time.