Journal of Engineering, Project, and Production Management, 2026, 16(4), 2026-90
An Emergency UAV Delivery Scheduling Method Combining MOPSO and Improved RRT Algorithms
Lecturer, School of
Huahu Airport Economics, Changjiang Polytechnic, Wuhan, 430074, China,
E-mail:
lib.libo@outlook.com
Project Management
Received January 21, 2026; revised May 6, 2026; accepted May 7, 2026
Available online May 29, 2026
Abstract: Emergency logistics scenarios pose dual demands for scheduling efficiency and system robustness in collaborative scheduling of multiple Unmanned Aerial Vehicles (UAVs). Traditional integer programming and heuristic methods have high computational complexity when applied to large-scale problems, making it difficult to meet real-time requirements. Therefore, this study constructs a UAV delivery scheduling method that integrates improved Multi-Objective Particle Swarm Optimization (MOPSO) and improved rapidly-exploring random tree. This method enhances the rationality of task allocation and the efficiency of path planning. In terms of methodology, the task allocation stage introduces dynamic weight adjustment. A partition-based fast non-dominated sorting mechanism is further employed to ensure convergence speed and distribution balance of the solution set. The path planning process combines predictive-guided sampling and cost-based extension functions. Smoothing processing is applied to further enhance trajectory quality and improve the success rate of obstacle avoidance. Experiments show that the UAV task allocation method reduces the total completion time to less than 100 minutes during the task allocation phase, with a load balancing degree exceeding 93%. The UAV path planning method maintains an obstacle avoidance success rate of around 92% in dense obstacle environments, and the path smoothness is close to 0.9, both of which are superior to those of the comparison algorithms. In the fusion experiment, the proposed emergency UAV distribution scheduling method achieves a task success rate of 96.3% in low disturbance environments, with an average delay of only twelve minutes. It still maintains a success rate of 94.8% in high disturbance environments, with the lowest energy consumption of 111.8 kJ (kJ is a standard unit of energy in the International System of Units (SI). One kilojoule equals 1,000 joules). Research shows that this fusion method exhibits significant advantages in efficiency, trajectory quality, and system robustness, providing a practical and feasible technical path for UAV intelligent scheduling in complex emergency scenarios.
Keywords: UAV scheduling, emergency logistics distribution, multi-objective particle swarm optimization, rapidly-exploring random tree, task allocation, path planning.
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Citation: Li, B. (2026). An Emergency UAV Delivery Scheduling Method Combining MOPSO and Improved RRT Algorithms. Journal of Engineering, Project, and Production Management, 16(4), 2026-90.
