## 1 Introduction  
The provision of vital medical services to save patients in emergency and post-disaster situations is an essential outreach activity in healthcare. Recent years have witnessed an increase in natural disasters, including North American Wildfire Season in 2021 [1], the Haiti Earthquake and Tropical Storm Grace in 2021 [2], the Atlantic Hurricane Season in 2021 [3], the COVID-19 pandemic in 2020 [4], the bushfires in Australia in 2019 [5], Cyclone Idai in Africa in 2019 [6], and both an earthquake and tsunami in Indonesia in 2018 [7]. Natural disasters present obstacles to healthcare workers attempting to provide essential treatments to patients and victims, which may exacerbate the spread of epidemics and increase the number of fatalities. To rescue people in emergency and post-disaster situations, healthcare services must be quickly provided to injured individuals. Such rescues can be accomplished by assigning patients to the nearest medical provider, medical institution, or community center as well as by sending essential medical deliveries, such as defibrillators and blood supplies, to the injured person's location. Most of the challenges related to accessing a patient's location are based on geographical characteristics such as dispersed islands, poor transportation infrastructure, and limited means of transport. These challenges tend to be particularly severe in both rural and developing countries. Moreover, when natural disasters occur, surrounding transportation infrastructure is often disrupted. For example, roads may become blocked because of natural disasters, or bridges may be broken [8]. To overcome these challenges and to provide necessary healthcare for patients in emergency and post-disaster situations, it is crucial to implement efficient methods to serve them. Ideally, transport means that it is not affected by damage to transportation infrastructure and that it is not significantly more expensive than ground transportation. Therefore, it is noteworthy that this can be achieved using unmanned aerial vehicles (UAVs), also known as drones. Drones have proven successful in many applications in rugged environments, including agriculture, aerial photography, and data collection [9; 10]. Drones have also been used in several healthcare applications (Figure 1). For example, drones have been used to spray chemicals in China to prevent the spread of COVID-19 [11]; to deliver medications during the Haitian earthquake of 2010 [12]; and to transfer blood samples [13], vaccines [14], and stool [15] to laboratories or wherever they are needed by patients. Importantly, healthcare applications demand specific considerations related to patients' individual situations to provide them with an ideal service. For example, people facing emergency situations may require devices such as defibrillators to quickly revive an injured person, or blood delivery may be needed within the shortest possible time while ensuring the validity of the supply [17]. In such cases, optimization of healthcare applications, given these considerations, is highly important [18; 19]. With the above considerations in mind, this study examines several problems faced by healthcare workers in emergency situations, namely: (i) the selection of drone launching centers (facilities) to maximize the ability of drones to serve patients (in this case, the drone centers must be selected from existing community centers); (ii) the distribution of available drones to drone launching centers; and (iii) the assignment of patients to drone launching centers and the available drones in those centers, considering the center's capacity and the range constraints of the drones. This problem has been referred to in the literature as the maximum-coverage facility location problem with drones (MCFLD) [20], which is a variant of the well-known Facility Location Problem (FLP). The FLP is a typical NP-hard combinatorial optimization problem [21; 22]. Usually, NP-hard problems are solved using heuristic oSr metaheuristic algorithms, rather than exact methods.