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This paper presents the Electric Vehicle Routing Problem with Drones (EVRPD), a novel strategy aimed at enhancing last-mile delivery through the combined use of Electric Vehicles (EVs) and drones. The primary objective is to reduce energy usage and CO2 emissions while maintaining effective delivery operations. The proposed system employs a diverse fleet where EVs act as mobile bases, and drones handle the final leg of parcel delivery. Both EVs and drones can deliver packages, with drones being utilized for smaller, time-sensitive shipments. The system emphasizes optimal payload distribution to boost energy efficiency and drone endurance. Drones are resupplied on the EVs’ rooftops via IoT technology. To address the routing challenge, the solution leverages bio-inspired algorithms, including Ant Colony Optimization (ACO) for EVs and Particle Swarm Optimization (PSO) for drones. This methodology enhances energy efficiency and drone autonomy, ensuring effective parcel delivery. By incorporating these advanced techniques, EVRPD presents a promising approach for a more sustainable and effective urban logistics network.

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Last-Mile Delivery Optimization Using Mixed Electric Vehicles, UAVs and Full Truck Delivery Based on Artificial Intelligence Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Bouhia Hanaa,
  • Jamal Benhra,
  • Wissal Ed-dehbi

摘要

This paper presents the Electric Vehicle Routing Problem with Drones (EVRPD), a novel strategy aimed at enhancing last-mile delivery through the combined use of Electric Vehicles (EVs) and drones. The primary objective is to reduce energy usage and CO2 emissions while maintaining effective delivery operations. The proposed system employs a diverse fleet where EVs act as mobile bases, and drones handle the final leg of parcel delivery. Both EVs and drones can deliver packages, with drones being utilized for smaller, time-sensitive shipments. The system emphasizes optimal payload distribution to boost energy efficiency and drone endurance. Drones are resupplied on the EVs’ rooftops via IoT technology. To address the routing challenge, the solution leverages bio-inspired algorithms, including Ant Colony Optimization (ACO) for EVs and Particle Swarm Optimization (PSO) for drones. This methodology enhances energy efficiency and drone autonomy, ensuring effective parcel delivery. By incorporating these advanced techniques, EVRPD presents a promising approach for a more sustainable and effective urban logistics network.