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<p>This study investigates the heterogeneous truck-drone delivery and pickup problem (HT-DDPP), with the objective of optimizing the collaborative paths of trucks and multiple drones for delivery and pickup services, while accounting for load capacity and range limitations. To achieve this goal, we propose a heuristic algorithm-driven four-step optimization strategy (HDFOS). First, a constrained k-means (Ck-means) clustering method is employed to partition the service region into sub-service regions that satisfy the load and range constraints of the drones. Second, an ant colony optimization algorithm (ACO) is utilized to determine the optimal truck path between these sub-service regions. Third, a genetic-particle swarm hybrid optimization algorithm (G-PSHA) is developed to compute the optimal delivery path for drones within each sub-service region, ensuring maximum speed advantage while adhering to range limitations. Finally, the ACO is applied to identify the optimal truck path for pickup tasks throughout the entire service region. Simulation and hardware-in-the-loop (HIL) experiments conducted across various scenarios validate the effectiveness and practicality of our proposed algorithm.</p>

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HDFOS: path planning for heterogeneous truck-drone cooperative delivery and pickup services

  • 期刊论文

  • 发表日期：

    2025年6月6日

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  • Wei Yue,
  • Baozhi Li,
  • Xiaoyong Zhang,
  • Qiaoran Yang

摘要

This study investigates the heterogeneous truck-drone delivery and pickup problem (HT-DDPP), with the objective of optimizing the collaborative paths of trucks and multiple drones for delivery and pickup services, while accounting for load capacity and range limitations. To achieve this goal, we propose a heuristic algorithm-driven four-step optimization strategy (HDFOS). First, a constrained k-means (Ck-means) clustering method is employed to partition the service region into sub-service regions that satisfy the load and range constraints of the drones. Second, an ant colony optimization algorithm (ACO) is utilized to determine the optimal truck path between these sub-service regions. Third, a genetic-particle swarm hybrid optimization algorithm (G-PSHA) is developed to compute the optimal delivery path for drones within each sub-service region, ensuring maximum speed advantage while adhering to range limitations. Finally, the ACO is applied to identify the optimal truck path for pickup tasks throughout the entire service region. Simulation and hardware-in-the-loop (HIL) experiments conducted across various scenarios validate the effectiveness and practicality of our proposed algorithm.