An Efficient Greedy Randomized Heuristic for the Maximum Coverage Facility Location Problem with Drones in Healthcare

Abstract

Recently, drones, have been utilized in many real-life applications including healthcare services. For example, providing medical supplies, blood samples, and vaccines to people in remote areas or during emergencies. In this study, the maximum coverage facility location problem with drones (MCFLPD) was studied. The problem is the application of drones in the context of the facility location and routing. It involves selecting the locations of drone launching centers, which maximizes patient service coverage within certain drone range constraints. In this study, a heuristic named the maximum coverage greedy randomized heuristic (MCGRH) is developed. The idea of the algorithm is to first choose some facilities to open at random from among those that can handle the most weight of the patient demands. After that, patients are assigned to the closest opened facility with the capacity to serve them. Finally, drones are assigned to patients based on the least amount of battery consumed between the patient and the facility. Extensive testing of MCGRH indicated that it ranks efficiently alongside other methods in the literature that tried to solve the MCFLPD. It was able to achieve a high coverage of patients (more than 80% on average) within a very fast processing time (less than 1 s on average).

Challenges for the Routine Application of Drones in Healthcare: A Scoping Review

“…Optimization techniques and route planning algorithms have been a focus of research to minimize travel time and energy consumption. A variety of novel techniques have proved efficient at maximizing patient coverage [56], reducing the number of UAVs used and the total routing distance [57], and leading to the better distribution of resources [60]. However, balancing fast delivery time and energy efficiency remains a challenge [56,57,60,61,65,73].…”

“…The framework also incorporates AI algorithms to enable intelligent decision-making processes for the UAV systems, allowing for the efficient real-time analysis of medical data collected through internet of medical things (IoMT) devices, leading to improved diagnosis and treatment options [58]. Also, a major part of the analyzed experimental research [56,57,60,61,65,73] deals with the solution and optimization of various routing and path planning problems, thus allowing for the whole drone system to minimize the travel time, reduce energy consumption, and improve overall efficiency. However, it is not always possible to achieve both a high speed and minimal energy consumption using the same algorithm, as in the experimental study by Al-Rabiaah et al [56].…”

“…Also, a major part of the analyzed experimental research [56,57,60,61,65,73] deals with the solution and optimization of various routing and path planning problems, thus allowing for the whole drone system to minimize the travel time, reduce energy consumption, and improve overall efficiency. However, it is not always possible to achieve both a high speed and minimal energy consumption using the same algorithm, as in the experimental study by Al-Rabiaah et al [56]. Their case study in the Portland metropolitan area showed that the developed novel heuristic had a higher average energy consumption (around 30%) compared to the Gurobi, greedy, and 3SH algorithms, even though it achieved the best coverage in the shortest time.…”

Joint Location–Allocation Model for Multi-Level Maintenance Service Network in Agriculture

“…Halper et al [44] concentrated on the mobile facility location problem and developed two local search neighbourhood algorithms to solve two subproblems in reality. Al-Rabiaah et al [45] developed a heuristic solution approach integrating the greedy search and maximum coverage approach to determine the location and allocation of drone launching centres. Kang [46] considered these NP-hard problem and proposed a heuristic solution method based on maximum flow to find the optimal location.…”

A public‐private partnership based model for regulating out‐of‐pocket expenditures to strengthen primary care system

“…In this section, a greedy based algorithm 52,53,62,[110][111][112] is provided for the given MILP. The algorithm deals with choosing one of the two available decisions.…”