Priority- and Risk-Aware Multi-UAV Relief Mission Planning Under Compound Uncertainty: A Dual-Risk-Land MBO Framework
Abstract
Relief planning with multiple unmanned aerial vehicles (UAVs) requires timely service to high-priority locations while maintaining flight feasibility under uncertain exposure and wind conditions. This study formulates a finite-scenario route-based model integrating repeated sorties, directional wind, terrain, payload-dependent energy, protected return reserve, critical-service lateness, deprivation tail risk, and worst-task waiting burden. Dual-Risk-Land Monarch Butterfly Optimization (DR-MBO) is evaluated against a standalone discrete MBO baseline, a safety-envelope extension (SE-DR-MBO), a problem-adapted implementation of adaptive large neighbourhood search (ALNS), and a biased random-key genetic algorithm (BRKGA) under a common decoder and matched budgets of distinct full scenario evaluations. Validation combines an exhaustively enumerated six-target decoder-induced search space, 30 paired runs on Noto Peninsula N14/N28/N42 cases, 24 synthetic controlled cases across two benchmark families, adaptive component ablations, computational scaling to 100 targets and 54 scenarios, and fixed-route replay over 295 observed Japan Meteorological Agency (JMA) wind hours. Relative to standalone MBO, DR-MBO reduces mean maximum critical-service lateness by 48.6%, 35.5%, and 33.1% on N14/N28/N42 and obtains better complete keys in 29/30, 29/30, and 30/30 paired runs; across the primary 12-case controlled benchmark it wins 157/180 MBO comparisons. Relative to ALNS, DR-MBO reduces Noto critical lateness by 51.5%, 42.5%, and 19.3% but uses 6.1–13.7% more energy. Under separate N28 hierarchy reoptimization, DR-MBO beats ALNS 14–1 with critical lateness first, whereas ALNS wins 15–0 with deprivation risk first. In the complementary cases, DR-MBO wins 57/60 paired complete keys against MBO and 37/60 against ALNS. Ablation results indicate interacting mechanisms rather than universal component dominance. The framework is therefore a routing-level planning tool; field-calibrated demand and physical UAV flight validation remain necessary before operational deployment.