## 2 Literature Review  
In this section, we explore and discuss state-of-the-art methods for solving the FLP for drone launching centers. Few studies have been conducted on FLP and its variants by using drones [24]. In fact, FLP has important applications in the field of drone usage, particularly in post-disaster situations or when seeking to enter locations that are difficult to access. Many issues must be considered when selecting the locations of drone launching centers, including the drone battery life, drone flying range, and other variables [25; 26]. In the field of healthcare, maintaining a short distance between the drone launching center and the patient is particularly important to enable a rapid medical response [27]. In this section, some efforts of prior researchers who have attempted to solve the FLP, as well as its variants, for the selection of drone-launching centers are reviewed. MCFLPD is an NP-hard problem that was introduced in 2019 by [PERSON] et al. [20] In the MCFLPD, the locations of the drone launching centers were selected from a network of prespecified capacitated facilities. The objective is to maximize the ability of drones to meet patient demands. In [20], three approaches were used to solve the problem: a mixed-integer programming solver, a novel greedy heuristic, and a three-stage heuristic (3 SH). The two heuristics (greedy and 3 SH) were proposed by the authors. The greedy heuristic first builds a weight matrix, where the rows represent instances of the demand point and the columns represent the facilities. Each element is filled by the percentage difference between the demand weight of a point and the battery consumed between the point and the facility. Subsequently, facilities are opened and specific demand points are assigned to them using the weight matrix. Then, the number of drones needed by each facility \(ND_{j}\) is defined according to the ratio of the total battery consumed between the facility and the demands assigned to it and the battery capacity of the drone. After this, opened facilities \(\hat{J}\) are sorted according to \(ND_{j}\). A drone from the available drones is sequentially assigned to each open facility. Finally, the demand assignment to the drone is performed according to the battery consumed between the demand and the facility. In contrast, the 3 SH proposed in [20] performs the following three steps: (i) solve a capacitated p-median facility location problem by trying to maximize the total weight, where the weight is determined by the percentage difference between the demand weight of a point and the battery consumed between the point and the facility; (ii) consider the assignment of the drones to facilities and the demand points to drones as a maximum-profit knapsack problem; and (iii) use a local exchange heuristic to improve the solution by randomly replacing a facility that has a low weight with another one. Extensive testing of the approaches proposed in [20], as well as a comparison, indicated that 3 SH had the best performance in terms of both time and solution quality. [PERSON] et al. [28] studied a problem derived from the FLP, in which the objective was to select drone ports to minimize the average distance to the locations of patient demand to be served by each port. A k-means algorithm was applied to cluster the demand areas. Next, several traveling salesman algorithms--2-opt, a genetic algorithm, exhaustive search, and ant colony optimization (ACO)--were applied to identify the preferred locations of drone ports to ensure that the demand in each cluster could be met by drones, as well as to ensure that the drone trips would involve the shortest routes. The ACO algorithm outperformed other algorithms investigated in this study. In another study, [PERSON] et al. [29] studied an FLP to identify an optimal solution for finding the locations of launching drones and recharging stations. Their objective was to minimize the total cost of the system.