A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment
Xinrun Xu, Zhanbiao Lian, Yurong Wu, Manying Lv, Zhiming Ding, Jian Yan, Shang Jiang
TL;DR
The paper tackles multi-constraint, multi-objective emergency resource allocation in IoT-enabled disaster settings by introducing the Multi-Objective Shuffled Grey Wolf-Frog Leaping Model (MSGW-FLM). The framework fuses Shuffled Frog Leaping and Grey Wolf Optimization, augmented with NSGA-II ranking and Levy-flight steps, to efficiently search the solution space across multiple cycles. Evaluations on 28 benchmark subtasks against NSGA-II, IBEA, and MOEA/D demonstrate MSGW-FLM's strong performance in several metrics, with notable gains in diversity and convergence. A rolling planning approach and random supply–demand tests further show MSGW-FLM’s potential to adapt plans dynamically as spatio-temporal data evolve, supporting timely and effective disaster relief distribution.
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.