Optimization of emergency material distribution routes in flood disaster with truck‐speedboat‐drone coordination

Abstract To improve the effectiveness of flood disaster relief operations, by ensuring timely and accurate delivery of urgently needed supplies to affected areas, this study focuses on the problem of emergency material distribution during floods. With the objective of minimizing the overall delivery...

Full description

Saved in:
Bibliographic Details
Main Authors: Ying Gong, Weili Wang, Yufeng Zhou, Jiahao Cheng
Format: Article
Language:English
Published: Wiley 2025-03-01
Series:Journal of Flood Risk Management
Subjects:
Online Access:https://doi.org/10.1111/jfr3.13045
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract To improve the effectiveness of flood disaster relief operations, by ensuring timely and accurate delivery of urgently needed supplies to affected areas, this study focuses on the problem of emergency material distribution during floods. With the objective of minimizing the overall delivery time of emergency materials, we propose a coordinated optimization model that integrates trucks, speedboats, and drones for effective distribution of emergency supplies in flood‐affected areas. To solve this optimization problem, we introduce an improved adaptive large neighborhood search (IALNS) algorithm, which builds on the traditional ALNS framework through refined tuning of deletion and insertion operators. Comparative analyses are conducted with a genetic algorithm, simulated annealing algorithm, and tabu search algorithm. The results reveal that the average performance gap of IALNS compared to these methods is 91.13%, 152.72%, and 16.92%, respectively. The experimental results demonstrate that the efficiency of the proposed model and algorithm in addressing the emergency supply distribution problem during flood disasters, highlighting the superior performance of IALNS. This research contributes to enhancing disaster response strategies, ultimately leading to improved outcomes for flood‐affected communities.
ISSN:1753-318X