Simulation-Based Optimization for Enhancing Preparedness of the Israel Fire Department
Simulation-Based Optimization for Enhancing Preparedness of the Israel Fire Department
Yaniv Leuchter, M.Sc. student at the department of Industrial
Advisor:Dr. Neta Rabin & Dr. Mor Kaspi
Abstract:
Effective resource allocation and emergency preparedness are crucial for the Israeli Fire and Rescue Authority, particularly in the face of urban growth, climate change, and major crises such as the COVID-19 pandemic and the Iron Sword War. Current tools, like Business Intelligence systems, fail to capture the complex interactions between fire stations and emergency incidents. This study addresses these gaps by developing a simulation model and optimization algorithms tailored to the Israeli Fire and Rescue Service. This research focuses on optimizing fire and rescue resource allocation to enhance operational efficiency and preparedness. We aim to answer three key questions: 1. Current resource effectiveness: Can existing fire station deployment and resources be allocated more efficiently? 2. Impact of resource expansion: Where should additional fire engines and resources be placed to maximize system effectiveness? 3. Future strategic planning: What resources should be acquired to meet projected service demands? The study follows a multi-phase methodology. First, we develop a high-resolution simulation model using multidimensional data to replicate operational dynamics, including incident locations, response times, resource utilization, and station interactions. This model forms the foundation for performance analysis and scenario-based decision-making. Next, we optimize resource allocation through an algorithm designed to minimize response times, maximize coverage, and improve efficiency. Machine learning techniques, particularly nonlinear dimensionality reduction, refine our approach by: • Identifying key variables influencing the simulation to focus search efforts on the most relevant components. • Using an approximation model to estimate simulation outcomes, reducing computational time during solution evaluation. Finally, we forecast future demand over 5, 10, and 20 years by analyzing historical trends and external factors such as urban development and climate change. These insights will guide resource planning and policy decisions. Findings will be translated into strategic recommendations for stakeholders, including the Israel Fire Commissioner, supporting data-driven resource acquisition and budget allocation. Funded by the Ministry of Science and Technology, this research highlights the importance of academia-government collaboration in emergency management.
Bio:
Yaniv Leuchter, M.Sc. student at the department of Industrial Engineering department Engineering in Tel Aviv University,specializing in Data Science. Yaniv holds a B.Sc. degree in Industrial Engineering from Tel Aviv University. His research, supervised by Professor Neta Rabin and Dr. Mor Caspi, focuses on optimizing resource allocation for emergency services using machine learning and simulation-based decision support systems. His work aims to enhance the preparedness and efficiency of fire and rescue services by integrating data-driven methodologies, predictive modeling, and algorithmic optimization.
