###### Abstract
We assume that fuel stations that can be resupplied by road can be used as refueling stations during the exploration. Thus, refueling stations can be supplied if a functional path from the DMC to the refueling station exists. Thus, at the beginning of the exploration, it is unknown which refueling stations could be used during the exploration. Motivated by this problem, we intend to answer the following research question:  
What are suitable strategies for refueling a UAV with a limited flying range under the uncertainty of the status of a disrupted network? To answer this research question, we use different networks, which are described next:  
**Known road network**  
The known road network refers to the road network of a rural region in pre-disaster conditions (see Figure 1). All locations in the region are connected by the road network, and there is at least one path between each pair of locations. The set of locations in the known road network are the DMC, road crossings, and victim locations. In this study, the DMC is the center of logistics operations, from where disaster information, fuel, and other resources are managed and humanitarian aid is deployed. Therefore, the DMC must be connected to the victim nodes by functional roads; otherwise, the victims are unreachable using the road network, and it makes no sense to conduct an exploration or plan a deployment. **Disrupted network**  
Functional parts of the known road network (see Figure 2). This network contains the same locations as the known road network. In the disrupted network, refueling stations at victim locations can be used for refueling the UAV, if they are connected to the DMC by functional roads. **Aerial network**The aerial network is a fully connected network. As shown in Figure 3, it contains all the locations of the known road and the disrupted network. All locations are connected to each other, including the disruption locations. The number of nodes and arcs in the network increases each time a disruption is detected. In this aerial network, the locations of the disruptions are included as disrupted locations. Thus, the number of nodes and edges in the aerial network increases along with the progress of the exploration. We extend the algorithm presented in the study [PERSON] et al. [4]. We propose three refueling strategies and different numbers of possible refueling station in the disrupted network. Our solution approach aims to balance the cost of considering a safety fuel buffer and the cost of performing a refueling operation every time it is needed. We extend the algorithm of [PERSON] et al. [4] by adding the following procedures: (1) Detection of refueling stations reachable by road from the DMC. This procedure determines which refueling stations are operating at each time \(t\) of the exploration. (2) Dynamic safety buffer. This procedure calculates the dynamic safety buffer in terms of distance. The dynamic buffer represents the distance that the UAV has to fly to explore the next selected unexplored road edge and then return to the closest reachable refueling station to the current UAV location. The safety buffer depends on the current location of the UAV and the closest refueling station, using an aerial edge. (3) A procedure that controls the UAV flying capacity. Depending on the refueling strategy defined in advance, the current location of the UAV, flying capacity, and the road network information, the algorithm makes two decisions: (a) it decides whether or not to perform a refueling operation, and (b) it decides at which station the refueling operation is most convenient. The refueling strategies under the different numbers of potential refueling stations are compared in extensive numerical studies using instances from the literature with different sizes and various disruption levels in the road network. Thus, the paper extends the existing literature as follows:  
* This paper extends the online algorithm of [PERSON] et al.