Dynamic Resource Management for Providing QoS in Drone Delivery Systems
Behzad Khamidehi, Majid Raeis, Elvino S. Sousa

TL;DR
This paper presents a deep reinforcement learning approach for dynamic drone-to-PDC allocation in drone delivery systems, ensuring QoS guarantees and efficient UAV utilization amid variable demand patterns.
Contribution
It introduces a queueing theoretic model combined with deep reinforcement learning to optimize UAV re-allocation with probabilistic QoS guarantees in drone delivery.
Findings
Outperforms baseline algorithms in diverse demand scenarios.
Maintains QoS constraints across all tested demand patterns.
Reduces average UAV usage while meeting service quality.
Abstract
Drones have been considered as an alternative means of package delivery to reduce the delivery cost and time. Due to the battery limitations, the drones are best suited for last-mile delivery, i.e., the delivery from the package distribution centers (PDCs) to the customers. Since a typical delivery system consists of multiple PDCs, each having random and time-varying demands, the dynamic drone-to-PDC allocation would be of great importance in meeting the demand in an efficient manner. In this paper, we study the dynamic UAV assignment problem for a drone delivery system with the goal of providing measurable Quality of Service (QoS) guarantees. We adopt a queueing theoretic approach to model the customer-service nature of the problem. Furthermore, we take a deep reinforcement learning approach to obtain a dynamic policy for the re-allocation of the UAVs. This policy guarantees a…
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Taxonomy
TopicsTransportation and Mobility Innovations · Vehicle Routing Optimization Methods · UAV Applications and Optimization
Methodstravel james
