## 6 Computational Results  
In our first numerical study, we are interested in the best strategy. We computed the exploration route for each instance and each seed with a fixed disruption level and \(\alpha\) value for all refueling strategies considering only the DMC as refueling station. Then, we determined the best exploration route, i.e., the exploration route with the lowest total travel time, for each instance, each seed, and each \(\alpha\) value. This exploration route is compared to all other routes of the same instance, seed, and each \(\alpha\) value to determine the best refueling strategy. In the next step, we count over all instance, seeds, and \(\alpha\) values how many times each strategy provides the best solution, i.e., the exploration route with the minimum travel time. Strategy ties were added independently to each of the strategies involved. Figure 8 presents the comparison between the strategies. The results indicate that with a percentage around 6%, 49%, and 45%, the best solutions are obtained with a _EConservative_, _MConservative_, and _Conservative_ refueling strategy, respectively. Thus, the best refueling strategy is _MConservative_, followed by _Conservative_, and the worst refueling strategy is _EConservative_. In the second stage of the numerical study, we computed the average duration of the exploration route for each instance and each disruption level for all \(\alpha\) values and refueling strategies. Then, we determined the average duration of the exploration route for each instance and each disruption level. In the next step, for each instance and each refueling strategy, we clustered the average duration of the exploration route by low, medium, and high disruption level. Then, these values were taken to compare average values over all instances. Figures 9-11 present for each refueling strategy the average duration of the exploration route for each instance and disruption level cluster. Figure 8: Global performance of refueling strategies. The moderately conservative refueling strategy _MConservative_ reports the best route exploration. This is especially clear for situations where the road network has a disruption level of up to 50%. For disruption levels of 50% and more, the performance of all strategies is similar. The similar performance is due to the longer time it takes for the UAV to find a functional edge connecting the DMC to the road network when disruption levels are higher, so finding a refueling station in addition to the DMC also takes longer. Therefore, many refueling operations are performed at the DMC. Depending on the characteristics of the road network, the number of refueling operations required varies slightly between refueling strategies. Figure 10: Performance of refueling strategies: normalized travel time of exploration route for instance cluster B. The standardized travel time 100% is equivalent to 57.52 h.  
Figure 9: Performance of refueling strategies: normalized travel time of exploration route for instance cluster A. The standardized travel time 100% is equivalent to 21.56 h.  
The last numerical study is to determine the impact of having multiple refueling stations: (1) only the DMC as refueling stations, (2) the DMC and 50% of the victim nodes operate as potential refueling stations, or (3) the DMC and all victim nodes operate as potential refueling stations. Remember that the victim locations operate as refueling stations only if they are known to be reachable from the DMC using the road network. We computed the average duration of the exploration route for each instance and each disruption level for all \(\alpha\) values and number of refueling stations. Then, we determined the average duration of the exploration route for each instance and each disruption level. In the next step, for each instance and refueling strategy, we clustered the average duration of the exploration route by low, medium, and high disruption levels. Then, these values were taken to compare average values over all instances.