gurobipy: Evaluation

Your task is to send to xnodet@uco.fr (and not any other email address!), before 2025-02-23, an email with:

Your name
the URL to a project on GitHub.com that contains a Python program solving the problem described below.

The layout of your project should be such that:

A file .python-version exists that has the Python version to be used.
A file requirements.txt exists with the Python libraries that are required for the project to run. See e.g. pip freeze documentation.
A file slideshow.sol does NOT exist.
A file slideshow.py exists and I can invoke it to solve the problem and create a file slideshow.sol in the current directory. It should accept an argument that is the path to a dataset from which to generate the solution.

If url is the value of that URL in your email, I will do, in a brand new Python 3.12 virtual environment, something similar to the following:

$ git clone [url] repo
$ cd repo
$ pip install --requirement requirements.txt
$ python slideshow.py [relative/path/to/dataset]
$ python [somewhere/only/i/know]/check_solution.py slideshow.sol

The file slideshow.sol that was generated by python slideshow.py should be in the format described below, and be an optimal solution to the problem described in the dataset, which may be one that you didn’t see.

Your Python code should be well structured and easy to understand. It should use gurobipy to create an optimization model and solve it. The shorter the time it takes to generate the model, the better.

You will find in the archive data.zip, in the folder project, a PDF file describing the problem to solve: hashcode_2019_qualification_round.pdf.

There are also, in this same directory, two datasets:

a trivial one to get you started: trivial.txt
one that will give you a potentially interesting problem to solve: PetPics-20.txt

Good luck!