A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917614687617024 |
|---|---|
| author | Xu, Xinrun Lian, Zhanbiao Wu, Yurong Lv, Manying Ding, Zhiming Yan, Jian Jiang, Shang |
| author_facet | Xu, Xinrun Lian, Zhanbiao Wu, Yurong Lv, Manying Ding, Zhiming Yan, Jian Jiang, Shang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10299 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment Xu, Xinrun Lian, Zhanbiao Wu, Yurong Lv, Manying Ding, Zhiming Yan, Jian Jiang, Shang Artificial Intelligence 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. |
| title | A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.10299 |