Drones for Medical Delivery Considering Different Demands Classes: A Markov Decision Process Approach for Managing Health Centers Dispatching Medical Products

• URL: http://arxiv.org/abs/2106.04729v1
• Date: Tue, 8 Jun 2021 23:20:31 GMT
• Title: Drones for Medical Delivery Considering Different Demands Classes: A Markov Decision Process Approach for Managing Health Centers Dispatching Medical Products
• Authors: Amin Asadi and Sarah Nurre Pinkley
• Abstract summary: We consider the problem of optimizing the distribution operations of a hub using drones to deliver medical supplies to different geographic regions. By considering different geographic locations, we consider different classes of demand that require different flight ranges. We classify the demands based on their distance from the drone hub, use a Markov decision process to model the problem and perform computational tests.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We consider the problem of optimizing the distribution operations of a hub using drones to deliver medical supplies to different geographic regions. Drones are an innovative method with many benefits including low-contact delivery thereby reducing the spread of pandemic and vaccine-preventable diseases. While we focus on medical supply delivery for this work, it is applicable to drone delivery for many other applications, including food, postal items, and e-commerce delivery. In this paper, our goal is to address drone delivery challenges by optimizing the distribution operations at a drone hub that dispatch drones to different geographic locations generating stochastic demands for medical supplies. By considering different geographic locations, we consider different classes of demand that require different flight ranges, which is directly related to the amount of charge held in a drone battery. We classify the stochastic demands based on their distance from the drone hub, use a Markov decision process to model the problem, and perform computational tests using realistic data representing a prominent drone delivery company. We solve the problem using a reinforcement learning method and show its high performance compared with the exact solution found using dynamic programming. Finally, we analyze the results and provide insights for managing the drone hub operations.

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