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

• URL: http://arxiv.org/abs/2403.10299v1
• Date: Fri, 15 Mar 2024 13:42:00 GMT
• Title: A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment
• Authors: Xinrun Xu, Zhanbiao Lian, Yurong Wu, Manying Lv, Zhiming Ding, Jian Yan, Shang Jiang,
• Abstract summary: We've developed the Multi-Objective Shuffled Gray Froging Model (MSGWFLM) This multi-objective resource allocation model has been rigorously tested against 28 diverse challenges. It's effectiveness is particularly notable in complex, multi-cycle emergency rescue scenarios.
• Score: 3.8572535126902676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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