Optimization of collaborative distribution path between geological disaster rescue transport vehicles and drones

Shen Liu; Yong Zeng; Xiaobo Xu

doi:10.1117/12.3030512

16 May 2024 Optimization of collaborative distribution path between geological disaster rescue transport vehicles and drones

Shen Liu, Yong Zeng, Xiaobo Xu

Author Affiliations +

Proceedings Volume 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024); 131601H (2024) https://doi.org/10.1117/12.3030512
Event: 4th International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 2024, Beijin, China

Abstract

To solve the problem of emergency material support for geological disaster rescue, a collaborative mode of transport vehicles and drones is applied for material distribution. Vehicles-drones collaborative path optimization model is established with the goal of minimizing the total distribution time. The K-means clustering analysis method is used to cluster the emergency material demand points in the disaster stricken area, in order to divide the distribution areas for the vehicle-drone groups, Genetic algorithm is used to code the task planning of the two transportation tools, and the damage repair operation in the adaptive large neighborhood search algorithm is embedded in the solution to complete the path optimization. Based on standard calculation examples, the results show that Vehicles-drones collaboration can reflect time advantages and meet the requirements of timeliness, accuracy, and sustainability in the delivery of emergency rescue tasks at the end.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shen Liu, Yong Zeng, and Xiaobo Xu "Optimization of collaborative distribution path between geological disaster rescue transport vehicles and drones", Proc. SPIE 13160, Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024), 131601H (16 May 2024); https://doi.org/10.1117/12.3030512

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