Emergency relief operations are essential in disaster aftermaths, necessitating effective resource allocation to minimize negative impacts and maximize benefits. In prolonged crises or extensive disasters, a systematic, multi-cycle approach is key for timely and informed decision-making. Leveraging advancements in IoT and spatio-temporal data analytics, we've developed the Multi-Objective Shuffled Gray-Wolf Frog Leaping Model (MSGW-FLM). This multi-constraint, multi-objective resource allocation model has been rigorously tested against 28 diverse challenges, showing superior performance in comparison to established models such as NSGA-II, IBEA, and MOEA/D. MSGW-FLM's effectiveness is particularly notable in complex, multi-cycle emergency rescue scenarios, which involve numerous constraints and objectives. This model represents a significant step forward in optimizing resource distribution in emergency response situations.

翻译：应急救灾行动在灾害发生后至关重要，需要有效的资源配置以最大限度地减少负面影响并提升效益。在长期危机或大规模灾害中，系统性的多周期方法对于及时且明智的决策制定尤为关键。借助物联网和时空数据分析技术的进步，我们开发了多目标混洗灰狼蛙跳模型（MSGW-FLM）。这一多约束、多目标资源分配模型经过了28种不同挑战的严格测试，在与NSGA-II、IBEA和MOEA/D等现有模型的对比中展现出更优性能。MSGW-FLM在处理包含众多约束与目标的复杂多周期应急救援场景时效果尤为显著。该模型在优化应急响应情境中的资源分配方面迈出了重要一步。