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.

利用物联网和时空数据分析，我们开发了多目标洗牌灰狼青蛙跳模型（MSGW-FLM），该模型在应对复杂的多周期紧急救援场景中显示出了卓越的性能，为优化应急救援资源分配迈出了重要的一步。