FelooPy

An integrated optimization environment (IOE) for AutoOR in Python!

🐍Introduction

FelooPy (FEasible, LOgical & OPtimal + Python) is a hyper-optimization interface that allows operations research scientists to build, develop, and test optimization models with almost all open-source and commercial solvers available. With FelooPy, the users would be able to benchmark the solvers to see if they meet the requirements.

Motivated by the AutoML era, which is "the process of automating the time-consuming, iterative tasks of machine learning model development," FelooPy is the first package that is going to provide an AutoOR tool for automating the time-consuming tasks of modeling, implementing, and analyzing an optimization model by providing an integrated (exact and heuristic) optimization environment in Python. Accordingly, FelooPy would also support multiple features such as sensitivity analysis, visualization, and more!

License: MIT

🔝 Advantages:

• An integrated optimization environment.
• Automating operations research model development workflow.
• Straightforward syntax for optimization modeling.
• Using only one syntax for coding.
• Having access to multiple optimization interfaces all at once.
• Using almost all solvers available.
• Getting execution time of your optimization model.
• Native support for sensitivity analysis.
• Native support for benchmarking all optimizers for your_model.
• Native support for heuristic optimization with the same syntax.
• Native support for solver interfaces (Coming soon)
• Native support for multi-objective optimization (Coming soon)
• ...

⬇️ Installation

There are multiple ways to install this Python package:

• Using the command pip install feloopy in a terminal.
• Using the command !pip install feloopy at the top of your code and implementing it for once.
• Using the following piece of code:

import os
os.system("pip install feloopy")

• Using the following function:

import pip

def install(package):
    if hasattr(pip, 'main'):
        pip.main(['install', package])
    else:
        pip._internal.main(['install', package])

install('feloopy')

• Using the package provided in the release section.

📄 Documentation

A short documentation is provided in the repository. Please refer to it for more information on installation and the usage of the FelooPy package.

🆘 Contributions

Are welcome :)

🎛️ Current contributors

⚡Support

This repository will be more and more complete over time.

Hence, it is kindly requested to support this work by giving a ⭐ to the repository to make it available for a broad range of audiences who are interested in using exact and heuristic optimization techniques in the Python programming language.

💫 Supporters

❤️ Sponsorship

Contact me on Twitter, or on Linkedin.