https://github.com/lawolf8/satellite-launch-cost-minimization-operations-research-

Using Operations Research to solve this problem, this takes inputs for various locations and provides the best day to launch a Low Earth Orbit (LEO) Satellite with cost minimization based on weather, size, cost, failure rate, and more.
https://github.com/lawolf8/satellite-launch-cost-minimization-operations-research-

api minimization nasa nws-api operationsresearch orbit prediction python satellite satellite-data

Last synced: about 1 month ago
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Using Operations Research to solve this problem, this takes inputs for various locations and provides the best day to launch a Low Earth Orbit (LEO) Satellite with cost minimization based on weather, size, cost, failure rate, and more.

• Host: GitHub
• URL: https://github.com/lawolf8/satellite-launch-cost-minimization-operations-research-
• Owner: lawolf8
• Created: 2024-01-29T13:13:00.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-09-09T14:03:23.000Z (3 months ago)
• Last Synced: 2024-09-10T16:47:26.165Z (3 months ago)
• Topics: api, minimization, nasa, nws-api, operationsresearch, orbit, prediction, python, satellite, satellite-data
• Language: Jupyter Notebook
• Homepage:
• Size: 6 MB
• Stars: 4
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Satellite-Launch-Cost-Minimization-Operations-Research

## Overview

This project leverages Operations Research techniques to solve the complex problem of minimizing the cost of launching Low Earth Orbit (LEO) satellites. By taking into account various factors such as location, weather, satellite size, cost, failure rate, and more, the goal is to identify the optimal day for a satellite launch. This personal project makes use of GCAT data and the National Weather API. Additionally, there is an interactive feature utilizing NWS's API to utilize the forecasted weather to determine the most cost-efficent site to conduct LEO launches within a 14-day period and limit failure rate.

## Key Features

- **Data Analysis**: Utilizes GCAT data for comprehensive satellite and launch information.

- **Weather Integration**: Incorporates data from the National Weather API to factor in historical and forecasted weather conditions. Weather per launch site is determined by the closest area recorded by NWS, this requires the great-circle distance between two points 

    on the Earth surface of both the areas recorded by NWS and the location of the launch sites.

- **Cost Optimization**: Focuses on minimizing overall launch costs considering multiple variables at various sites. 

- **Failure Rate Assessment**: Evaluates the risk associated with satellite launches.

## Packages/APIs

Python 3.11.1

- Gurobipy (Operations Research Solver)

- Pandas

- Geopandas

- Numpy

- Matplotlib

- Seaborn

- National Weather Service API (NWS)

## How It Works

1. **Input Gathering**: Collects data on potential launch locations, satellite specifications, satellite costs, and desired launch windows.

2. **Weather Data Integration**: Fetches weather forecasts from the National Weather API for considered locations and dates.

4. **Optimization Algorithm**: Applies Operations Research algorithms to determine the most cost-effective launch day, balancing factors like weather conditions, satellite size, and failure rates.

5. **Output**: Provides a detailed report on the best day for the satellite launch, including cost analysis and risk assessment.

6. **User Interface**: Determines the ideal location to commit a LEO launch within 14 days