A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment
CoRR(2024)
摘要
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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