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This research aims to enhance emergency material scheduling in floods using aviation resources.

April 29, 2026Evaluation Review0 citations

Modeling for Effective Aviation Emergency Scheduling in Flood-Stricken Areas: A Material Demand Urgency Evaluation Approach

XGXinyao GuoZhengzhou University of Aeronautics JLJiayi LiZhengzhou University of Aeronautics JLJintao LuShanxi University of Finance and Economics

Key Points

This research aims to enhance emergency material scheduling in floods using aviation resources.
Integrated CRITIC and cloud model theory to evaluate material demand urgency.
Developed a mathematical model for a single aviation emergency rescue center using an optimized ant colony algorithm.
Incorporated a penalty function for a multi-objective aviation scheduling model to minimize time and cost.
Applied an improved genetic algorithm to solve the scheduling model.
Used the case of rainstorm-induced floods in Zhengzhou, China, as an empirical study.
Aircraft deliveries are prioritized based on material demand urgency.
All tasks were completed within the required time using four optimized routes.
Although total time increased, economic costs were significantly reduced.
Disaster loss was mitigated by effective scheduling.

Abstract

Knowing how to implement emergency material scheduling and transportation during emergency rescues, such as major and critical emergencies, has become a research hotspot in academia and industry in recent years. To leverage the speed and terrain-insensitive advantages of aviation, the weak limitations of geographical conditions must be addressed, and the material scheduling efficiency of aviation rescue centers in disaster-stricken areas needs to be improved. In this study, CRITIC and cloud model theory were integrated to evaluate the urgency of emergency material demands in different flood-stricken areas under catastrophic flood disaster risks. Furthermore, a mathematical model for a single aviation emergency rescue center to dispatch emergency materials to multiple disaster-stricken sites was designed based on the optimized ant colony algorithm. A penalty function was then incorporated to formulate a multi-objective aviation scheduling model, aiming to minimize both total rescue time and total cost. The model was solved using an improved genetic algorithm. Taking rainstorm-induced flood disasters in the megacity of Zhengzhou, China, in 2021 as the empirical research case, the operating paths for the aviation emergency rescue center to serve multiple demand points were optimized. The impact of material demand urgency on scheduling decisions was analyzed. Results revealed that when material demand urgency is considered, aircraft complete deliveries according to urgency rankings and return to the center. All tasks can be completed within the required time via four routes. Although total time increases, economic cost is significantly reduced, and disaster loss is mitigated. The findings obtained from this study provide a decision-making reference for improving the efficiency of aviation emergency rescue and enhancing urban risk management capabilities in response to major and critical emergencies.

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