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A Risk-Averse Two-Stage Stochastic Programming Model for Emergency UAV Task Allocation
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DOI：10.3390/drones10070529.png)
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Abstract
En 中文
As UAVs are increasingly used in emergency rescue, task allocation under uncertainty still faces tail delay risk. Existing studies mainly optimize expected cost and pay insufficient attention to task temporal relations and delay losses under extreme scenarios. To address this issue, this study develops a risk-averse two-stage stochastic programming model that incorporates the precedence relation between reconnaissance and delivery tasks, UAV routes, and task execution sequences into a unified decision process. The first stage determines task assignment, route selection, and visit order, while the second stage evaluates waiting, delay, and recourse costs under stochastic scenarios. A Mean-CVaR risk measure is adopted to characterize both average performance and tail risk. To solve the resulting multi-scenario risk-averse model, this study develops a problem-tailored Enhanced BD framework based on the classical Benders decomposition structure. The proposed framework integrates partial scenario embedding, heuristic warm start, and dynamic cut-pool management to strengthen early master problem information, improve feasible-route search, and control the growth of scenario-wise cuts. Numerical experiments based on a Nanjing emergency rescue instance evaluate the model and algorithm in terms of solution performance, acceleration ablation, optimized scheduling results, and parameter sensitivity. The results show that the proposed model can identify tail delay risk concentrated at a small number of demand points and downstream nodes in task chains. Across ten independent replications, Enhanced BD achieves a higher convergence success rate and lower final BD Gap than Basic BD in the medium-sized and largest tested instances. Parameter analysis shows that moderate risk aversion improves out-of-sample performance, whereas excessive risk aversion or resource allocation may reduce overall scheduling efficiency. The proposed method improves tail risk identification and solution capability for emergency UAV task allocation under time uncertainty and provides a methodological reference for risk-aware UAV emergency scheduling.
Keywords:
emergency UAV task allocation
two-stage stochastic programming
Mean-CVaR
risk aversion
Benders decomposition
tail delay risk
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