phil85 / MPFCC-Algorithm

A model-based algorithm for the fair-capacitated clustering problem

MPFCC-Algorithm

A model-based algorithm for the fair-capacitated clustering problem.

Dependencies

The MPFCC-Algorithm depends on:

• Gurobi
• Numpy
• Scipy

Gurobi is a commercial mathematical programming solver. Free academic licenses are available here.

Installation

1. Download and install Gurobi (https://www.gurobi.com/downloads/)
2. Clone this repository (git clone https://github.com/phil85/MPFCC-Algorithm.git)

Usage

The main.py file contains code that applies the MPFCC-algorithm to an illustrative example.

labels = mpfcc(X, colors, number_of_clusters, max_cardinality, min_balance,
               random_state=24, mpfcc_time_limit=300)

Reference

Please cite the following paper if you use this algorithm.

Tran, V. Kammermann, M., Baumann, P. (2023): The MPFCC algorithm: a model-based approach for fair-capacitated clustering. In preparation

Bibtex:

@inproceedings{baumann2020clustering,
	author={Vanessa Tran and Manuel Kammermann and Philipp Baumann
	title={The MPFCC algorithm: a model-based approach for fair-capacitated clustering},
	year={2023},
	note={In preparation},
}

License

This project is licensed under the MIT License - see the LICENSE file for details