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The Dynamic Drone Scheduling Delivery Problem
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OA
AI
DOI：10.1007/978-3-031-16579-5_18.png)
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Abstract
En 中文
Logistics plays an important role in today's last-mile economy. Therefore, companies constantly seek for improving their delivery system towards more efficient and sustainable management of parcel distribution. In this paper, we study the Dynamic Drone Scheduling Delivery Problem. The objective is to minimize the delayed deliveries by a fleet of drones located in a central drone station, taking into account the uncertain arrival of parcels, soft time windows, and energy requirements. We develop a Markov Decision Processes (MDP) formulation and solve it approximately by implementing a value-based Reinforcement Learning (RL) approach. We compare our approach with several heuristic dispatching policies and provide insights into the efficiency of our RL algorithm when facing different delivery scenarios.
Keywords:
Drone scheduling
Battery charging
UAV
Last mile
Reinforcement learning