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Smart Disaster Management Minimizing Response Time in Disaster Situations Using AI
Abstract
Rescue Team Scheduling Mode is an essential part of disaster management that facilitates timely deployment of emergency response teams with optimal utilization of resources. Conventional scheduling models are likely to fail in accommodating dynamic environments, uncertain disaster scenarios, and real-time decision-making. To address these challenges, a robust scheduling algorithm is presented, which combines machine learning, reinforcement learning, and optimization techniques. The algorithm takes into account factors like the severity of emergencies, availability of teams, current traffic conditions, and environmental limitations to distribute resources optimally. Using algorithms such as Multi-Objective Genetic Algorithm (MOGA), Ant Colony Optimization (ACO), and Reinforcement Learning (RL), the model redistributes teams adaptively and optimizes routes. The suggested mode of scheduling also provides equitable load distribution, reduces response time, and improves coordination among different agencies.
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