## 5 Results
### Platform Simulation with Focused Failure  
Sixty-eight simulations of the abandonment process were executed by both simulators on the dynamic graph emulating the platform under study, whose coincident vertices with the main ladder from bow to port were disabled as a result of the failure we configured. Likewise, the analysis of the starting situations of GQ and 3 TW were addressed.  
The following diagrams (see Figure 7), show the resulting evacuation times achieved by both simulators (MaritimeEXODUS and SIMONE) for the specific GQ and 3 TW starting situations.  
The evacuation times obtained for the starting GQ situation show the following. It is a regulated protocol, repeatedly rehearsed by the embarked population. However, Figure 7 illustrates that the value of the median evacuation times obtained from the MaritimeEXODUS and SIMONE simulators are noticeably different (422 s, 253 s, respectively), as well as their corresponding dispersion. It should be noted that the GQ assumption alludes to an ideal situation where the members of the embarked population are located at certain predefined positions, although under this assumption, the network of routes is significantly affected by the configured fault. This reduces the agility of the evacuation (see Figure 8) compared to the same starting situation without breakdowns (as shown in Figure 5).  
Figure 7: Box plots, abandonment times (MaritimeEXODUS vs. SIMONE) for the 3 TW and GQ starting situations.  
Regarding the evacuation times attained for the 3 TW starting situation, the following comments can be made. Once again, most of the members of the embarked population start the evacuation from very specific decks. This causes delays at the beginning of the process in terms of reaction times and preparation for evacuation. This estimated time is not included in the results of the simulations. We also identified agents crowded on the departure decks waiting to evacuate, inevitably leading to congestion at the accesses. Therefore, these common locations of specific decks show disproportionate occupancy rates. This situation calls for optimal management to distribute the agents among the accesses of the starting ship decks to equalize the estimated costs at the congestion points (accesses). This means progressively evacuating agents throughout the entire process (see Figure 9), minimizing the time taken by each agent and, thus, the time spent by the last agent leaving the platform. A significant difference between the results achieved by MaritimeEXODUS and SIMONE is observed (see Figure 7), showing the competitive advantage of the SIMONE algorithm. SIMONE increases the probability of survival by reducing median (430 s, 294 s, respectively) evacuation times.  
Figure 8: Dispersion diagram, time per number of evacuees (GQ situation, SIMONE).  
Figure 9: Dispersion diagram, abandonment time per number of evacuees (3 TW, SIMONE).  
We can conclude that, based on the evacuation times (see Figure 7) provided by MaritimeEXODUS and SIMONE, there are notable differences between the evacuation times of the GQ and 3 TW study cases. The optimal management implemented by SIMONE provides a great competitive advantage that leads to a notable increase in the probability of survival for the embarked population.