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.
@article{arxiv.2403.10299,
title = {A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment},
author = {Xinrun Xu and Zhanbiao Lian and Yurong Wu and Manying Lv and Zhiming Ding and Jian Yan and Shang Jiang},
journal= {arXiv preprint arXiv:2403.10299},
year = {2024}
}