## 6 Computational Results
Figures 12-14 present for each number of refueling stations the average travel time savings of the UAV for each refueling strategy, disruption level, and instance cluster. We computed the worst exploration route over all \(\alpha\) values for each instance, seed, disruption level, and refueling strategy, i.e., the time-longest exploration route. The worst solution of each instance, seed, disruption level, and refueling strategy is compared to the other solutions of the same instance, disruption level, refueling strategy, and number of refueling stations. In the next step, we calculated the difference between the total travel time of the worst solution and the total travel time of the other solutions. The difference is calculated for each refueling strategy; the difference is conceived of as a savings in travel times that can be achieved depending on the number of refueling stations in the disrupted network. Considering the clusters of instances in Table 2, we calculated the average savings for each level of disruption, refueling strategy, and number of refueling stations over all instances, seed, and \(\alpha\) values. The following is the analysis of the results for each refueling strategy. The results show that for road networks with a low, medium, and high level of disruption, using the refueling strategy _EC Conservative_ (Figures 12-14, _EC Conservative_), the worst exploration route is achieved when considering 100% of the victim locations as refueling stations. These results are argued on the fact that there is a high chance that victim nodes are accessible in networks with a low level of disruption. Therefore, every time a disruption is detected, the exploration route is redirected to the last accessible explored node in the disrupted road network, being a victim node or road crossing. Thus, each route redirection can lead to a refueling operation, increasing the exploration duration. Thus, the results show  
Figure 11: Performance of refueling strategies: normalized travel time of exploration route for instance cluster C. The standardized travel time 100% is equivalent to 38.23 h.  
that for instances in cluster A and C with a low disruption level using refueling strategy _EC Conservative_, the best solution is achieved when only the DMC operates as refueling station. Similar for instances in cluster A and B with a medium disruption level, the best solution is achieved when only the DMC operates as a refueling station. The results for networks with a high disruption level using the _EC Conservative_ refueling strategy differ among the clustering of instances. The best solutions for instances in cluster A are obtained when 50% of the victim locations operate as potential refueling stations. For cluster B, the best solutions are also achieved when 50% of the victim locations operate as potential refueling stations. For instances in cluster C with high disruption levels, the best solution is achieved when only the DMC operates as a refueling station. Similarly, we analyzed the results of the _MC Conservative_ and _Conservative_ refueling strategies (Figures 12-14, _MC Conservative_ and _Conservative_). The worst solution occurs when only the DMC is considered as the refueling station for the _MC Conservative_ and _Conservative_ refueling strategies. This is because every time a refueling operation is needed, the UAV has to fly back to the DMC. The best solution for the _EC Conservative_ and _Conservative_ refueling strategies is achieved when 100% of the victim locations are considered as refueling stations. In conclusion, the refueling strategy _MC Conservative_ is the best strategy to deal with flying range limitations for small and large instance clusters with a low disruption. For small and large instance clusters with a high disruption level, the best refueling strategy is the strategy _Conservative_.