YusufAlperenDonmez/GTU-Graduation-Project-1-Efficient-Deployment-Of-UAVs-For-Disaster-Management: initial release

UAV Deployment for Disaster Management Simulation This project provides a comprehensive simulation and optimization system for the efficient deployment of Unmanned Aerial Vehicles (UAVs) for disaster management. The methodology is based on a Multi-Criterion Optimization Model utilizing Integer Linear Programming (ILP) and heuristic algorithms. Project Goal The primary objective of this project is to develop a comprehensive decision-support tool that can: Determine the optimal locations for UAVs to act as mobile helping units in disaster scenarios (floods, earthquakes, fires). Maximize user connectivity while minimizing both the number of deployed UAVs and the deployment cost. Provide a desktop-based GUI for interactive scenario building, parameter adjustment, and comparative results visualization. Key Features Multi-Objective Optimization: Balances four competing objectives: minimizing UAV count, maximizing user connections, minimizing user-to-UAV distance, and minimizing cost. Interactive Simulation Interface: Allows users to configure grid environments and constraints directly using a Tkinter-based dashboard. Dual Optimization Methods: Provides solutions using both: Optimal Solution: Uses Integer Linear Programming (ILP) solved via Branch & Bound (B&B) for mathematically optimal results. Proposed Heuristic: A low-complexity greedy algorithm for rapid decision-making in time-critical scenarios. Performance Analysis: Includes a batch simulation feature to compare algorithms across different connectivity targets ( β ). Visual Analytics: Generates comparative bar charts for connection counts, installed UAVs, and weighted utility scores. Usage How to Use the Application: Configure Inputs: Set the simulation parameters on the left panel, including Grid Size (e.g., 4x4), Number of Victims (N), and UAV Cost/Budget. Define Constraints: Adjust the Load Balancing constraints ( γ m i n and γ m a x ) and the Maximum Signal Radius. Run Simulation: Click the "RUN BATCH SIMULATION" button. This executes the solvers across a range of Beta values (0.2 to 0.6). Visualize Map: Switch to the "Map Visualization" tab to see the spatial deployment of UAVs and user connections. Use the slider to toggle between different Beta scenarios. Analyze Results: Switch to the "Performance Analysis" tab to view detailed charts comparing the Optimal vs. Proposed Heuristic methods. Model Outputs The optimization model generates the following key outputs: The total number of UAVs installed to cover the disaster area. The total number of user connections established. A Weighted Utility Score that combines cost, distance, and coverage into a single metric. Runtime analysis comparing the speed of the Optimal vs. Heuristic algorithms. A full visualization of active UAV locations and their connections to specific users. Acknowledgements This project was developed as a Graduation Project for the CSE 495 / 496 courses at Gebze Technical University. Project Supervisor: Prof. Dr. Didem Gözüpek Kocaman Project Student: Yusuf Alperen Dönmez