A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment

Xinrun Xu, Zhanbiao Lian, Yurong Wu, Manying Lv, Zhiming Ding +2 more
3/15/2024
cs.AI

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Abstract

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.

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Cite this paper

@article{xu2024multiconstraint,
  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},
  year   = {2024},
  eprint = {2403.10299},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.10299v1}
}

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