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• W3200480647 abstract "With the developments in information technologies and the intense use of online commerce, the use of drones in distribution process has gained importance. In order to transport products to more than one location, drones can perform the distribution by following a specific route, as in the traveling salesman problem. Drones provide advantages over land transportation since they are not affected by the traffic congestion and can be used autonomously. However, the limited battery durations increase the importance of using the optimum route in distribution processes. In this study, it is aimed to use drones in drug distribution. Nowadays, due to the Covid-19 pandemic, it is aimed to distribute the drugs for the patients in an optimum way with drones. In this study, it is aimed to find the optimized routes for drones in drug distribution since Covid-19 medicine distribution is a time-critic mission. Since the number of patients in a certain area may increase very quickly, it is ensured that the patients are divided into clusters and the optimum route is determined for each cluster. We propose a hybrid model consisting of a combination of K-means clustering and Ant Colony algorithms. In particular, Covid-19 patients use the mobile part of the developed application on their smartphones and transmit their medication requests to our central server. We have compared the performance of Ant Colony, Artificial Bee and Genetic algorithm metaheuristics at the stage of determining the most suitable route according to the demands collected dynamically on the central server. In the process of determining the most suitable route, Ant Colony algorithm yields the closest to optimum results for different location groups. We have developed the mobile and web site of the application to validate the proposed drug delivery model." @default.
• W3200480647 created "2021-09-27" @default.
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• W3200480647 date "2021-09-30" @default.
• W3200480647 modified "2023-09-29" @default.
• W3200480647 title "Route Optimization for Medication Delivery of Covid-19 Patients with Drones" @default.
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• W3200480647 doi "https://doi.org/10.29109/gujsc.930903" @default.
• W3200480647 hasPublicationYear "2021" @default.
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