## 6 Conclusions  
The delivery of medical supplies and the provision of aid to patients using drones can significantly contribute to improving healthcare services. This study aims to provide high-quality solutions within a reasonable time for the maximum coverage facility location problem with drones (MCFLPD), which is more complex than the traditional facility location problem. State-of-the-art methods in the literature have been proposed to solve this problem, including a Gurobi mixed-integer programming solver, which gives acceptable-quality solutions in terms of coverage but has an unacceptably long running time to find a feasible solution; a greedy heuristic, which is extremely fast but achieves low coverage compared to the other methods; and a three-stage heuristic (3 SH) algorithm, which achieves around 96.6% of Gurobi's coverage within approximately 27 s.  
In this paper, we propose a new heuristic called the maximum coverage greedy randomized heuristic (MCGRH) to solve MCFLPD. MCGRH is distinguished by the following: (i) the selection of facilities to be opened at random from those that can serve the largest number of patients with the highest demand; (ii) the assignment of patients to the nearest open facilities that are available to serve them; and (iii) the assignment of drones to the opened facilities that have the highest demands, as well as the assignment of patients of those facilities to those with the lowest battery consumed. We compared MCGRH with the state-of-the-art methods mentioned above, considering both time performance and solution quality. We used the Wilcoxon signed-rank test for statistical comparison, with the results indicating that the MCGRH excelled in solution quality (i.e., coverage), together with time performance. In particular, MCGRH produced a coverage of more than 80% (approximately 98.6% of Gurobi's average coverage) in less than 1 s of average processing time.  
In future work, we intend to improve the MCGRH to reduce the total energy consumed while maintaining high coverage within a short processing time, besides considering obstacle avoidance. Moreover, it is possible to explore new variants of the problem that may reflect more realistic healthcare applications, such as making battery stations available to recharge a drone's battery during its trip.  
Conceptualization, S.A.-R., M.H. and S.A.; methodology, S.A.-R.; software, S.A.-R.; validation, S.A.-R.; formal analysis, S.A.-R.; investigation, S.A.-R.; resources, S.A.-R.; data curation, S.A.-R.; writing--original draft preparation, S.A.-R.; writing--review and editing, M.H. and S.A.; visualization, S.A.-R.; supervision, M.H. and S.A.; project administration, M.H. and S.A.; funding acquisition, S.A.-R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Deanship of Scientific Research, King Saud University.  
Not applicable.  
Data Availability StatementData presented in this study are available in ScienceDirect at [[https://doi.org/10.1016/j.trc.2018.12.001](https://doi.org/10.1016/j.trc.2018.12.001)]([https://doi.org/10.1016/j.trc.2018.12.001](https://doi.org/10.1016/j.trc.2018.12.001)) [20] (accessed on 29 December 2021).  
The authors would like to thank the Deanship of Scientific Research at King Saud University for funding and supporting this research through the initiative of DSR Graduate Students Research Support (GSR).  
The authors declare no conflict of interest.