{"id":27437393,"url":"https://github.com/ryomendev/uav-resourceallocation-major-project","last_synced_at":"2025-04-14T20:29:03.307Z","repository":{"id":280923980,"uuid":"943629125","full_name":"RyomenDev/UAV-resourceAllocation-major-project","owner":"RyomenDev","description":"The UAV Resource Allocation project uses Particle Swarm Optimization (PSO) to efficiently assign UAVs for delivering essential resources like medicine, food, and weapons to multiple outposts while minimizing energy consumption and ensuring high-priority deliveries. 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The goal is to efficiently allocate UAVs to deliver essential resources such as **medicine, food, and weapons** to outposts while minimizing energy consumption and ensuring high-priority deliveries.\n\n## Features\n✅ **Takes input for:**\n- **n**: Number of outposts\n- **m**: Number of UAVs\n- **UAV details**: Weight capacity, energy/km, total energy\n- **Outpost details**: Resource needs (medicine, food, weapons), coordinates (x, y)\n- **Base station coordinates**\n\n✅ **Uses PSO to optimize assignments:**\n- Minimizes energy consumption\n- Prioritizes urgent resource deliveries\n- Allocates UAVs based on travel distance and energy constraints\n\n## How it Works\n1. **Input Processing**: Reads the number of UAVs, outposts, and their respective details.\n2. **Initialization**: Generates an initial swarm of potential UAV allocation solutions.\n3. **Particle Movement**: Iteratively updates solutions based on velocity, local best, and global best solutions.\n4. **Fitness Evaluation**: Computes energy consumption and ensures UAVs meet resource constraints.\n5. **Convergence**: Continues optimization until an optimal or near-optimal allocation is found.\n\n## Installation \u0026 Usage\n### **Prerequisites**\n- C++ Compiler (GCC, Clang, or MSVC)\n- C++ Standard Library\n\n### **Compilation \u0026 Execution**\n```bash\n g++ -o uav_allocation main.cpp -std=c++17\n ./uav_allocation\n```\n\n\u003c!-- ### **Input Format**\n```\nNumber of Outposts: 3\nNumber of UAVs: 2\nBase Station: (0,0)\nUAV 1: Weight Capacity = 10, Energy/km = 2, Total Energy = 100\nUAV 2: Weight Capacity = 15, Energy/km = 3, Total Energy = 120\nOutpost 1: Medicine = 5, Food = 3, Weapons = 2, Location (x, y) = (5, 5)\nOutpost 2: Medicine = 4, Food = 6, Weapons = 1, Location (x, y) = (10, 10)\n```\n\n### **Output Example**\n```\nOptimal UAV Assignments:\nUAV 1 -\u003e Outpost 1 (Energy Used: 20)\nUAV 2 -\u003e Outpost 2 (Energy Used: 30)\nTotal Energy Consumption: 50\n``` --\u003e\n\n## Applications\n🚀 **Disaster Response** – Optimally deploy UAVs to deliver medical aid and supplies\n🚀 **Military Operations** – Efficiently allocate UAVs for supply distribution in remote areas\n🚀 **Logistics \u0026 Supply Chain** – Automating resource delivery using UAV fleets\n\n## Future Possible Improvements\n- 🔹 Incorporate **multi-objective optimization** (e.g., delivery time, fuel efficiency)\n- 🔹 Implement **hybrid metaheuristic algorithms** (e.g., Genetic Algorithm + PSO)\n- 🔹 Extend to **real-time dynamic allocation** with changing outpost demands\n- 🔹 UAV **recharge stations** \n- 🔹 **multi-stop deliveries**\n\n## License\nThis project is open-source under the **MIT License**.\n\n## Contributors\n- **Akash Mishra** (Developer \u0026 Researcher)\n- **Biranjay Kumar Gupta** (Developer \u0026 Researcher)\n- **Sunny Kumar** (Developer \u0026 Researcher)\n\nFor queries, reach out via GitHub or email.\n\n---\n🚀 **Optimizing UAV resource allocation using AI-driven heuristics!** 🚀\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fryomendev%2Fuav-resourceallocation-major-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fryomendev%2Fuav-resourceallocation-major-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fryomendev%2Fuav-resourceallocation-major-project/lists"}