Summary of A Multi-constraint and Multi-objective Allocation Model For Emergency Rescue in Iot Environment, by Xinrun Xu and Zhanbiao Lian and Yurong Wu and Manying Lv and Zhiming Ding and Jian Yan and Shang Jiang
A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment
by Xinrun Xu, Zhanbiao Lian, Yurong Wu, Manying Lv, Zhiming Ding, Jian Yan, Shang Jiang
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Multi-Objective Shuffled Gray-Wolf Frog Leaping Model (MSGW-FLM) is a novel approach to resource allocation in disaster relief operations. By leveraging IoT and spatio-temporal data analytics, MSGW-FLM outperforms established models like NSGA-II, IBEA, and MOEA/D in complex emergency scenarios with multiple constraints and objectives. The model has been rigorously tested against 28 diverse challenges and shows superior performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a disaster aftermath, it’s crucial to allocate resources effectively to minimize harm and maximize benefits. A new approach called MSGW-FLM uses internet-of-things (IoT) data and advanced analytics to make better decisions. This model is particularly helpful in emergency situations that involve multiple objectives and constraints. |
