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https://github.com/ryomendev/uav-resourceallocation-minor-project

project models an Unmanned Aerial Vehicle (UAV) resource allocation system to optimize the delivery of essential resources (medicine, food, and weapons) to various outposts.
https://github.com/ryomendev/uav-resourceallocation-minor-project

algorithms cpp efficiency oops oops-in-cpp resource-allocation uav

Last synced: 1 day ago
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project models an Unmanned Aerial Vehicle (UAV) resource allocation system to optimize the delivery of essential resources (medicine, food, and weapons) to various outposts.

Host: GitHub
URL: https://github.com/ryomendev/uav-resourceallocation-minor-project
Owner: RyomenDev
Created: 2024-10-16T05:45:15.000Z (3 months ago)
Default Branch: main
Last Pushed: 2025-01-13T06:17:01.000Z (12 days ago)
Last Synced: 2025-01-13T07:20:42.336Z (12 days ago)
Topics: algorithms, cpp, efficiency, oops, oops-in-cpp, resource-allocation, uav
Language: C++
Homepage:
Size: 10.4 MB
Stars: 2
Watchers: 1
Forks: 0
Open Issues: 1
Metadata Files:
- Readme: readMe.md

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README

# Efficient Allocation of Unmanned Aerial Vehicles (UAVs)

## Table of Contents
1. [Introduction](#introduction)
2. [Problem Definition](#problem-definition)
3. [System Design and Implementation](#system-design-and-implementation)
4. [Algorithmic Approach](#algorithmic-approach)
- [Graph Representation and Dijkstra’s Algorithm](#graph-representation-and-dijkstras-algorithm)
- [Priority Ordering](#priority-ordering)
- [UAV Selection and Allocation](#uav-selection-and-allocation)
- [Weight and Delivery Calculations](#weight-and-delivery-calculations)
5. [Optimization and Results](#optimization-and-results)
6. [Code Architecture](#code-architecture)
7. [Future Enhancements](#future-enhancements)
8. [Conclusion](#conclusion)

---

## Introduction
Efficient allocation of UAVs is essential for scenarios where resources must be transported from a central base to multiple outposts with varying demands and deadlines. This system uses Object-Oriented Programming (OOP) principles and graph-based algorithms to optimize the allocation process.

---

## Problem Definition
The aim is to transport resources such as medicine, food, and weapons from a central base to multiple outposts efficiently. Challenges include:
- Meeting varying demands and deadlines.
- Minimizing delivery times.
- Efficiently utilizing UAVs while adhering to constraints like weight, speed, and travel distance.

---

## System Design and Implementation
This project implements:
- A graph representation for calculating optimal routes.
- Priority-based delivery scheduling.
- Capacity-based UAV allocation.

---

## Algorithmic Approach
### Graph Representation and Dijkstra’s Algorithm
- Represent the delivery area as a graph with nodes (locations) and edges (routes).
- Use Dijkstra’s Algorithm to calculate the shortest path from the base to each outpost.

### Priority Ordering
- Prioritize deliveries based on deadlines, urgency, and demand.

### UAV Selection and Allocation
- Select UAVs based on capacity, speed, and distance constraints.
- Allocate resources to maximize delivery efficiency.

### Weight and Delivery Calculations
- Ensure UAVs operate within weight and distance limits.
- Handle partial deliveries if outpost demands exceed UAV capacity.

---

## Optimization and Results
- Minimized delivery times.
- Efficient UAV utilization.
- Adherence to operational constraints.

---

## Code Architecture
1. **Graph Class:**
- Represents locations and routes.
- Implements Dijkstra’s Algorithm.

2. **UAV Class:**
- Encapsulates UAV properties like capacity, speed, and range.

3. **DeliveryManager Class:**
- Manages resource allocation and scheduling.
- Prioritizes deliveries and allocates UAVs.

4. **Main Program:**
- Coordinates the system.
- Reads input data and invokes classes for processing.

---

## Future Enhancements
- Integration with real-time traffic and weather data.
- Dynamic UAV reallocation for emergency deliveries.
- Support for additional constraints like energy consumption.

---

## Conclusion
This system efficiently allocates UAVs to deliver resources to outposts while adhering to constraints. It minimizes delivery times, meets deadlines, and ensures optimal resource utilization.