Industrial Innovations

Industrial Innovations

Optimization of Resource Allocation for Disaster Management Based on Artificial Intelligence and Smart Cities

Document Type : Original Article

Authors
Farshad Kaveh 1
Mahdi Karbasian 2
Omid Boyer 3
Hadi Shirouyehzad 3
1 Department of Industrial Engineering, Na.C., Islamic Azad University, Najafabad, Iran
2 Department of Industrial Engineering, Malek ashtar University of Technology, Tehran, Iran
3 Department of Industrial Engineering, Na.C., Islamic Azad University, Najafabad
10.61882/jii.2.3.301
Abstract
Disaster management in metropolitan areas, particularly in the context of earthquakes, is a major challenge in the modern era that requires effective strategies for optimal resource allocation. This study presents a hybrid model that integrates artificial intelligence and metaheuristic algorithms with a multi-objective framework to improve decision-making in disaster scenarios. The primary objective of the model is to minimize risk, cost, and response time simultaneously. In the first stage, two machine learning algorithms, namely K-Nearest Neighbor (KNN) and Extreme Gradient Boosting (XGBoost), are applied to predict earthquake impacts across the 22 districts of Tehran. The prediction process considers three critical indicators: degree of urban deterioration, population density, and infrastructure intensity. After assessing classification performance using accuracy, precision, recall, and f1-score, the best-performing algorithm is selected, and its outputs are employed as inputs to the mathematical optimization model. The proposed mathematical model is solved using a combination of exact optimization methods and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The findings demonstrate that NSGA-II outperforms the exact approach in terms of computational efficiency. Furthermore, integrating machine learning predictions into the optimization framework significantly improves the objective functions by reducing risk, lowering total costs, and enhancing service response time during post-disaster operations. Sensitivity analysis further reveals that increased resource demand exerts the most substantial influence on the risk objective function. Among the predictive features, population density is identified as the most dominant factor in determining earthquake impact, while urban deterioration and infrastructure intensity exhibit relatively equal but less significant effects. Consequently, population density emerges as the most influential feature in the proposed machine learning-based disaster management model for smart cities.
Keywords
Artificial Intelligence
Metaheuristic Algorithm
Smart City
Multi-objective Optimization
Disaster Management
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Industrial Innovations
Volume 2, Issue 3 - Serial Number 7
Summer 2024
Pages 301-319

  • Receive Date 19 August 2025
  • Revise Date 30 August 2025
  • Accept Date 09 September 2025