Truck-Drone Collaborative Emergency Goods Delivery Path Optimization Based on Dual-Particle-Ant Algorithm
In emergency situations such as natural disasters and transportation disruptions, truck-drone collaborative delivery demonstrates significant advantages. By constructing a collaborative delivery path optimization model with variable truck capacity for drones, a bi-level particle-ant algorithm combining multi-objective particle swarm optimization (MOPSO) and ant colony optimization (ACO) is proposed. Through numerical experiments, the MOPSP-AOC algorithm outperforms the traditional NSGA2 algorithm. Sensitivity analysis reveals that truck mobility has a significant impact on the optimization of the latest delivery time and cost. This study provides an efficient and effective solution for truck-drone collaborative emergency supply delivery.
Authors
- Shouhuan Zhou (ORCID: https://orcid.org/0000-0002-8487-0913)
- Yutian Liu (ORCID: https://orcid.org/0000-0001-6932-5207)
- Kui Zhu (ORCID: https://orcid.org/0000-0001-8242-3952)
- Shaoning Sun
Institutions
- China Agricultural University (CN)
Publication Details
- Published
- 2025-03-21
- DOI
- https://doi.org/10.1109/icaace65325.2025.11019855
- Primary Topic
- Robotic Path Planning Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00