## 4 Results and Discussion
### Theoretical and Comparative Evaluation of the Model  
Various studies have shown that the implementation of systems based on artificial intelligence and heuristic optimization in logistics environments can generate significant improvements in operational efficiency [6; 11; 26]. Previous research has shown that the use of multi-agent models in port infrastructure management can reduce waiting times and improve the use of available resources [26]. For example, in automated port terminals, the use of route planning algorithms such as A* and Dijkstra has made it possible to reduce transit times by 15-30% compared to traditional models based on fixed rules [27]. Likewise, the integration of dynamic scheduling systems in Ro-Ro terminals has shown that the optimization of boarding times can reduce downtime in the loading and unloading flow by up to 20% [28]. These findings support the feasibility of the approach proposed in this study, providing a solid theoretical basis for comparison with traditional port management models. Below in the Table 6 shows a comparison with the expected theoretical metrics. Figure 4: A complete multi-agent system. A theoretical comparison was made with traditional port management models, showing improvements in:  
* Space allocation: Reduction of occupancy time by up to 25%. * Route optimization: Less unnecessary travel and energy savings. * Traffic management: Dynamic adaptation to congestion. * Synchronized boarding: Avoid unnecessary waiting. * Proactive monitoring: Reduction of operational errors through AI. The theoretical analysis suggests that the proposed multi-agent system has the potential to improve the operational efficiency of the Ro-Ro terminal, aligning with the principles of Industry 4.0. These principles include digitalization, real-time decision-making, interoperability, and autonomous management [1]. The evaluation of the multi-agent model has been carried out through a theoretical comparative analysis based on three key approaches: (1) the comparison with previous studies on optimization in logistics and port terminals, (2) the evaluation of standard times and processes in traditional port management models, and (3) the validation of the algorithms used, whose efficiency has been demonstrated in various logistics applications. First, the literature supports that the use of heuristic algorithms in the allocation of spaces can significantly reduce the occupancy time in port fields. Likewise, route optimization using algorithms such as A* and reinforcement learning has shown improvements in energy efficiency and reduced travel times in logistics environments. In addition, the use of predictive models for traffic management and boarding synchronization allows dynamic adaptation to congestion and operational schedules, reducing unnecessary waiting and operational errors. These improvements, based on previously validated optimization methodologies, provide a solid basis for theoretical comparison and justify the applicability of the multi-agent system in improving the operational efficiency of Ro-Ro terminals within the framework of Industry 4.0. The proposed multi-agent system presents a viable approach to the intelligent management of Ro-Ro terminals; however, its implementation in a real environment requires the consideration of various operational and technological factors. Firstly, the adoption of this model requires integration with existing port management systems, which implies compatibility with digital infrastructures and operational planning platforms. Many termi